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Is GPT Powerful Enough to Analyze the Emotions of Memes?: Discussion and References

Authors:
(1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA;
(2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA;
(3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA;
(4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA;
(5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Table of Links Abstract & Introduction Related Work Methodology Experiment Result Discussion and References IV. EXPERIMENT RESULTS In this section, we conducted an in-depth investigation by using the two datasets aforementioned in section 3 to explore the effectiveness of GPT in analyzing sentiments in memes. After carefully evaluating each meme based on the prompts designed, we meticulously collated and analyzed the results. Here is the comprehensive analysis of our findings. Case 1: The results of Facebook Hateful Memes dataset Our initial investigation involved the evaluation of GPT’s proficiency in identifying hateful content within memes using Facebook’s Hate/Non-Hate Classification Dataset. Here we repeat the experiments four times with different prompts. Hateful Content Identification: The task of identifying hateful memes posed a significant challenge. It required the AI to understand not only the content of the image and the accompanying text but also their nuanced interplay. The results showed an accuracy rate of 39% in detecting hateful content with a relatively high standard deviation of 6.87%. It can be seen that the performance of GPT is far less accurate in classifying the hateful memes, and the different prompts would affect the decision of GPT considerably. To accurately flag hateful memes, a model needs to be adept at grasping not only the overt message but also the undertones, context, and subtext. The 39% accuracy rate thus provides us with a baseline for improvement and future model refinements. Non-Hateful Content Identification: On a brighter note, GPT’s performance in recognizing non-hateful memes was much stronger, with an impressive accuracy rate of 80% and much lower standard deviation of 2.69%. The high accuracy suggests that GPT is quite adept at discerning memes that are not hateful, and the different prompts used had minimal impact on its decision-making process. Case 2: Memotion analysis dataset Overall Sentiment Classification: the first task in this series was classifying the overall sentiment of a meme as positive, negative, or neutral. In this regard, GPT achieved an accuracy rate of 79% for positive and 35% for negative. It can be observed that this result is consistent with the performance in the classification of hateful memes in the Facebook dataset, which shows confidence in understanding the positive sentiment in a meme, based on both the visual content and the accompanying text. For negative (for example, offensive in the later paragraph) content detection, the low accuracy still suggests the challenge. Humor recognition: GPT achieved a 60% accuracy rate in the task of recognizing humor. This outcome is notably remarkable, considering the intricate nature of humor as a multifaceted human emotion, often subject to cultural, social, and individual perspectives. The considerable level of accuracy exhibited by GPT indicates its ability to comprehend the humorous aspects present in a diverse range of memes. This result also implies that the GPT has acquired the ability to recognize various indicators of humor to a certain degree. These indicators might be obvious, such as punchlines or visual gags, or implicit, such as irony or absurdity. Sarcasm recognition: the model’s performance in the domain of sarcasm recognition was notably lower, registering an accuracy of 45% as illustrated in Table 2. Though lower, this accuracy level is acceptable given the inherent difficulties of sarcasm detection. Sarcasm often involves expressing the opposite of what is meant, requiring an understanding of context, tone, and frequently shared cultural knowledge. This observation helps to emphasize the limits of the approach in accurately detecting more subtle emotional cues and contextual subtleties. The lower accuracy rate observed in sarcasm recognition highlights the need for further study and model enhancement in this area. Offensive recognition: as seen in Table 2, GPT’s accuracy stood at 37% in recognizing offensive content, presenting the weakest performance. As aforementioned in overall sentiment classification, GPT meets the huge challenge in offensive content recognition. The consistency of this finding illustrates the difficulty associated with detecting offensive content. Comparable to hateful meme classification, offensive content in memes may exhibit subtlety, sometimes concealed within humor or sarcasm, or manifest through a complex interplay of textual and visual elements that can be hard to interpret for an AI. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA; (2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA; (3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA; (4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA; (5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Authors: (1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA; (2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA; (3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA; (4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA; (5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Work Related Work Methodology Methodology Experiment Result Experiment Result Discussion and References Discussion and References IV. EXPERIMENT RESULTS In this section, we conducted an in-depth investigation by using the two datasets aforementioned in section 3 to explore the effectiveness of GPT in analyzing sentiments in memes. After carefully evaluating each meme based on the prompts designed, we meticulously collated and analyzed the results. Here is the comprehensive analysis of our findings. Case 1: The results of Facebook Hateful Memes dataset Case 1: The results of Facebook Hateful Memes dataset Our initial investigation involved the evaluation of GPT’s proficiency in identifying hateful content within memes using Facebook’s Hate/Non-Hate Classification Dataset. Here we repeat the experiments four times with different prompts. Hateful Content Identification: The task of identifying hateful memes posed a significant challenge. It required the AI to understand not only the content of the image and the accompanying text but also their nuanced interplay. The results showed an accuracy rate of 39% in detecting hateful content with a relatively high standard deviation of 6.87%. It can be seen that the performance of GPT is far less accurate in classifying the hateful memes, and the different prompts would affect the decision of GPT considerably. To accurately flag hateful memes, a model needs to be adept at grasping not only the overt message but also the undertones, context, and subtext. The 39% accuracy rate thus provides us with a baseline for improvement and future model refinements. Non-Hateful Content Identification: On a brighter note, GPT’s performance in recognizing non-hateful memes was much stronger, with an impressive accuracy rate of 80% and much lower standard deviation of 2.69%. The high accuracy suggests that GPT is quite adept at discerning memes that are not hateful, and the different prompts used had minimal impact on its decision-making process. Case 2: Memotion analysis dataset Case 2: Memotion analysis dataset Overall Sentiment Classification: the first task in this series was classifying the overall sentiment of a meme as positive, negative, or neutral. In this regard, GPT achieved an accuracy rate of 79% for positive and 35% for negative. It can be observed that this result is consistent with the performance in the classification of hateful memes in the Facebook dataset, which shows confidence in understanding the positive sentiment in a meme, based on both the visual content and the accompanying text. For negative (for example, offensive in the later paragraph) content detection, the low accuracy still suggests the challenge. Humor recognition: GPT achieved a 60% accuracy rate in the task of recognizing humor. This outcome is notably remarkable, considering the intricate nature of humor as a multifaceted human emotion, often subject to cultural, social, and individual perspectives. The considerable level of accuracy exhibited by GPT indicates its ability to comprehend the humorous aspects present in a diverse range of memes. This result also implies that the GPT has acquired the ability to recognize various indicators of humor to a certain degree. These indicators might be obvious, such as punchlines or visual gags, or implicit, such as irony or absurdity. Sarcasm recognition: the model’s performance in the domain of sarcasm recognition was notably lower, registering an accuracy of 45% as illustrated in Table 2. Though lower, this accuracy level is acceptable given the inherent difficulties of sarcasm detection. Sarcasm often involves expressing the opposite of what is meant, requiring an understanding of context, tone, and frequently shared cultural knowledge. This observation helps to emphasize the limits of the approach in accurately detecting more subtle emotional cues and contextual subtleties. The lower accuracy rate observed in sarcasm recognition highlights the need for further study and model enhancement in this area. Offensive recognition: as seen in Table 2, GPT’s accuracy stood at 37% in recognizing offensive content, presenting the weakest performance. As aforementioned in overall sentiment classification, GPT meets the huge challenge in offensive content recognition. The consistency of this finding illustrates the difficulty associated with detecting offensive content. Comparable to hateful meme classification, offensive content in memes may exhibit subtlety, sometimes concealed within humor or sarcasm, or manifest through a complex interplay of textual and visual elements that can be hard to interpret for an AI. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Is GPT Powerful Enough to Analyze the Emotions of Memes?: Experiment Result

Authors:
(1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA;
(2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA;
(3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA;
(4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA;
(5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Table of Links Abstract & Introduction Related Work Methodology Experiment Result Discussion and References Abstract Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have demonstrated their ability in a multitude of tasks. This project aims to explore the capabilities of GPT-3.5, a leading example of LLMs, in processing the sentiment analysis of Internet memes. Memes, which include both verbal and visual aspects, act as a powerful yet complex tool for expressing ideas and sentiments, demanding an understanding of societal norms and cultural contexts. Notably, the detection and moderation of hateful memes pose a significant challenge due to their implicit offensive nature. This project investigates GPT’s proficiency in such subjective tasks, revealing its strengths and potential limitations. The tasks include the classification of meme sentiment, determination of humor type, and detection of implicit hate in memes. The performance evaluation, using datasets from SemEval-2020 Task 8 and Facebook hateful memes, offers a comparative understanding of GPT responses against human annotations. Despite GPT’s remarkable progress, our findings underscore the challenges faced by these models in handling subjective tasks, which are rooted in their inherent limitations including contextual understanding, interpretation of implicit meanings, and data biases. This research contributes to the broader discourse on the applicability of AI in handling complex, context-dependent tasks, and offers valuable insights for future advancements. Index Terms—memotion analysis, hateful memes detection, GPT model I. INTRODUCTION Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have recently gathered substantial interest from both academia and industry. Due to their exceptional capacity to comprehend, generate, and engage in complex linguistic tasks, these models are revolutionizing the development and application of AI algorithms. Equipped with sophisticated algorithms and vast training datasets, LLMs have demonstrated advanced conversational capabilities, processing textual input and output to participate in detailed and meaningful dialogues [6], [10], [11]. OpenAI’s ChatGPT [12] is a prominent representative among these LLMs. It is an AI chatbot backed by the generative pretrained transformer (GPT) model and has attracted a broad societal interest. The proficiency of ChatGPT is particularly noticeable in the realm of question answering, spanning multiple sectors including, but not limited to, healthcare [7] and finance [8]. This model uses its linguistic capabilities to understand questions and generate human-like answers, marking a crucial milestone in AI-driven communication. Nevertheless, despite these impressive feats, LLMs, including GPT, confront considerable challenges when addressing subjective tasks, one such task being the interpretation and annotation of internet memes. Internet memes, modern digital artifacts, encompass a variety of cultural and sociological attitude, and serve as a type of social shorthand among online communities [1], [2]. These memes blend verbal and graphic elements to express complex concepts or feelings in a concise manner. However, the multimodal nature of memes, which combine various images with informal, often humorous or ironic text, introduces an intricate layer of complexity. To comprehend and analyze memes, one must manage the subtle interplay of visual clues, linguistic content, cultural allusions, and common community knowledge. This complexity, along with how subjective memes can be, makes it really tough for LLMs to accurately interpret memes and analyze their sentiment. Sentiment Analysis (SA) primarily concentrate on interpreting user sentiments from textual content. However, due to the proliferation and pervasiveness of multimodal social media data, such as online memes, SA must diversify to handle multimodal data sources. This diversification becomes even more complex when considering ’hateful memes’, which cleverly combine text and images to spread offensive or harmful messages under the guise of humor [3]–[5]. Detecting and moderating such content not only requires understanding the text-image interaction but also a grasp of the cultural context and, in many cases, specific sub-cultural expertise. These challenges call for ongoing research and development in the field, pushing the boundaries and exploring the potential of LLMs like GPT. In this project, we focus on the capabilities and limitations of LLMs like GPT in dealing with subjective and nuanced tasks, particularly internet meme sentiment analysis. This exploration forms the crux of this research, contributing to the broader discussion on the evolving potential and limitations of AI. In this project, we delve into the intricacies of subjectivity, assessing GPT’s performance in tasks necessitating an understanding of societal norms and cultural contexts. Our investigation primarily revolves around two research questions: 1. Can GPT accurately detect implicitly hateful memes given a specific prompt? 2. Can GPT effectively conduct sentiment analysis of memes, including classifying the sentiment of each meme as positive or negative, and categorizing the type of humor into three sub-classes: sarcastic, humorous, and offensive? We draw upon datasets from Facebook’s hateful memes [3] and SemEval-2020 Task 8: Multimodal Memotion Analysis [9] and for this project. The experimental section of the study will present a comparative analysis highlighting the constraints and limitations of GPT’s responses. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA; (2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA; (3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA; (4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA; (5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Authors: (1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA; (2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA; (3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA; (4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA; (5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Work Related Work Methodology Methodology Experiment Result Experiment Result Discussion and References Discussion and References Abstract Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have demonstrated their ability in a multitude of tasks. This project aims to explore the capabilities of GPT-3.5, a leading example of LLMs, in processing the sentiment analysis of Internet memes. Memes, which include both verbal and visual aspects, act as a powerful yet complex tool for expressing ideas and sentiments, demanding an understanding of societal norms and cultural contexts. Notably, the detection and moderation of hateful memes pose a significant challenge due to their implicit offensive nature. This project investigates GPT’s proficiency in such subjective tasks, revealing its strengths and potential limitations. The tasks include the classification of meme sentiment, determination of humor type, and detection of implicit hate in memes. The performance evaluation, using datasets from SemEval-2020 Task 8 and Facebook hateful memes, offers a comparative understanding of GPT responses against human annotations. Despite GPT’s remarkable progress, our findings underscore the challenges faced by these models in handling subjective tasks, which are rooted in their inherent limitations including contextual understanding, interpretation of implicit meanings, and data biases. This research contributes to the broader discourse on the applicability of AI in handling complex, context-dependent tasks, and offers valuable insights for future advancements. Index Terms —memotion analysis, hateful memes detection, GPT model Index Terms I. INTRODUCTION Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have recently gathered substantial interest from both academia and industry. Due to their exceptional capacity to comprehend, generate, and engage in complex linguistic tasks, these models are revolutionizing the development and application of AI algorithms. Equipped with sophisticated algorithms and vast training datasets, LLMs have demonstrated advanced conversational capabilities, processing textual input and output to participate in detailed and meaningful dialogues [6], [10], [11]. OpenAI’s ChatGPT [12] is a prominent representative among these LLMs. It is an AI chatbot backed by the generative pretrained transformer (GPT) model and has attracted a broad societal interest. The proficiency of ChatGPT is particularly noticeable in the realm of question answering, spanning multiple sectors including, but not limited to, healthcare [7] and finance [8]. This model uses its linguistic capabilities to understand questions and generate human-like answers, marking a crucial milestone in AI-driven communication. Nevertheless, despite these impressive feats, LLMs, including GPT, confront considerable challenges when addressing subjective tasks, one such task being the interpretation and annotation of internet memes. Internet memes, modern digital artifacts, encompass a variety of cultural and sociological attitude, and serve as a type of social shorthand among online communities [1], [2]. These memes blend verbal and graphic elements to express complex concepts or feelings in a concise manner. However, the multimodal nature of memes, which combine various images with informal, often humorous or ironic text, introduces an intricate layer of complexity. To comprehend and analyze memes, one must manage the subtle interplay of visual clues, linguistic content, cultural allusions, and common community knowledge. This complexity, along with how subjective memes can be, makes it really tough for LLMs to accurately interpret memes and analyze their sentiment. Sentiment Analysis (SA) primarily concentrate on interpreting user sentiments from textual content. However, due to the proliferation and pervasiveness of multimodal social media data, such as online memes, SA must diversify to handle multimodal data sources. This diversification becomes even more complex when considering ’hateful memes’, which cleverly combine text and images to spread offensive or harmful messages under the guise of humor [3]–[5]. Detecting and moderating such content not only requires understanding the text-image interaction but also a grasp of the cultural context and, in many cases, specific sub-cultural expertise. These challenges call for ongoing research and development in the field, pushing the boundaries and exploring the potential of LLMs like GPT. In this project, we focus on the capabilities and limitations of LLMs like GPT in dealing with subjective and nuanced tasks, particularly internet meme sentiment analysis. This exploration forms the crux of this research, contributing to the broader discussion on the evolving potential and limitations of AI. In this project, we delve into the intricacies of subjectivity, assessing GPT’s performance in tasks necessitating an understanding of societal norms and cultural contexts. Our investigation primarily revolves around two research questions: 1. Can GPT accurately detect implicitly hateful memes given a specific prompt? 2. Can GPT effectively conduct sentiment analysis of memes, including classifying the sentiment of each meme as positive or negative, and categorizing the type of humor into three sub-classes: sarcastic, humorous, and offensive? We draw upon datasets from Facebook’s hateful memes [3] and SemEval-2020 Task 8: Multimodal Memotion Analysis [9] and for this project. The experimental section of the study will present a comparative analysis highlighting the constraints and limitations of GPT’s responses. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Is GPT Powerful Enough to Analyze the Emotions of Memes?

Workplaces are filled with moments when it’s easy to lose your calm. Uncontrollable, sudden, and intense emotions that overwhelm you, can dramatically and unexpectedly lead to an emotional outburst. Negative emotions like anger, fear, or frustration show up when your expectations aren’t met or people say or do things that conflict with your personal values and aspirations. Ignoring the conflict or whatever’s bothering you for too long creates inner turmoil—it makes you react to other people or the things in your environment. You act without thinking. You become a of your thoughts and circumstances. victim Not being in control of your emotions can make you say or do things that you regret later. You may shout at a coworker. Say impolite things to a junior member of your team. Burst into tears when others disagree with you. These emotional outbursts may not only lead to extreme distress; they’re also damaging to your relationships at work. Others can’t see the inner conflict bubbling up inside—they can’t make sense of why you are calm one moment and agitated the next. No doubt they’ll find your reaction inappropriate and unnecessary. What’s done is done. You can’t go back in time and fix your overreaction, but you can certainly build emotional agility—move on with curiosity, courage, and compassion. Emotional agility is not only about keeping your emotions in check and not lashing out your frustrations at others, it’s also about what you do once you react in unintended ways. Here are the 5 practices to prevent strong emotions from overwhelming you, take control of the situation, and do damage control if you do end up acting out in an unhealthy way: Instead of Emotional Avoidance, Lean Toward Emotional Acceptance Hundreds of thoughts will run through your mind after acting in undesirable ways: Everyone will think I am crazy. No one will speak to me. I will not get more opportunities. All these thoughts can consume so much of your time and energy that you can fail to connect with your emotions. You may avoid negative emotions with the hope that they will go away. But avoidance is like a prison. It prevents you from experiencing critical life lessons. You fail to build the skills necessary to deal with discomfort. Every repeated cycle of negative emotions leading to avoidance makes it powerful and weakens your ability to deal with difficult situations. Noam Shpancer, professor of psychology at Otterbein University and a clinical psychologist specializing in the treatment of anxiety disorders says — “Attempts at avoiding negative emotions are usually futile. Telling yourself that a certain emotion is intolerable or dangerous traps you in constant vigilance regarding the very thing you're trying to avoid. You become hyper-vigilant about any possibility of this feeling arising. The fear of the impending negative experience becomes a negative experience in itself.” This also applies to how you deal with your emotions after losing your calm at work. Instead of confronting your feelings, you may believe that the best way to deal with them is to put them out of your mind. But ignoring or avoiding makes these feelings much harder to overcome while acknowledging them can result in real personal growth. Instead of stifling or pushing them away, what if you learn to accept your emotions? Accepting does not make them true. It just allows your emotions to be what they are without judging them or trying to change them. Seeing the emotion for what it is without attempting to get rid of it shifts you from emotional avoidance towards emotional acceptance. Noam Shpancer calls emotional acceptance a far better strategy than avoidance because accepting, acknowledging, and absorbing them makes you spend less energy in pushing the emotion away, and instead, pursue the behaviors that are aligned with your goals and values. He says “Emotions, when viewed as part of a spectrum of available sources of information, are a bit like the weather report. They are important to know, consider, and understand, but they are not necessarily the overriding factor in your life plans. When the weather is bad (not to your liking), it doesn't mean you have to deny it, focus all your attention on it, or cancel your plans because of it. What you need to do is accept the weather and adjust your plans accordingly.” So, the first step to dealing with an emotional outburst at work is to come in touch with your real emotions—become fully aware of them as they show up. Mindfulness guides us to become more emotionally agile by allowing us to observe the thinker having the thoughts. Simply paying attention brings the self out of the shadows. It creates the space between thought and action that we need to ensure we’re acting with volition, rather than simply out of habit. But mindfulness is more than knowing ‘I’m hearing something’, or being aware ‘I’m seeing something’, or even noticing ‘I’m having a feeling’. It’s about doing all this with balance and equanimity, openness and curiosity, and without judgement. It also allows us to create new, fluid categories. As a result, the mental state of mindfulness lets us see the world through multiple perspectives, and go forward with higher levels of self-acceptance, tolerance and self-kindness. — Susan David Mindfulness will not only help you better manage your emotions after losing your calm; it will enable you to avoid such emotional outbursts in the future—learning to be present in the moment without judgment and observing your feelings without being overwhelmed can create space for better understanding and acceptance of your emotions, making it easier to manage them effectively. Name the Emotion to Tame the Emotion After an emotional outburst, you’re bound to feel some very strong emotions. It’s not enough to acknowledge them. You also need to give words to your feelings. In other words, say to yourself, out loud, what negative emotion you’re experiencing, as you’re experiencing it. For example: I am feeling embarrassed. I am feeling ashamed. I am feeling angry at myself. Simply naming it is going to calm you down. This technique introduced by psychologist Dan Siegel creates a bit of space between you and that emotion. Naming your emotions tends to diffuse their charge and lessen the burden they create. There’s another advantage of naming emotions this way. Matthew Lieberman is a Professor of Psychology at the University of California Los Angeles (UCLA). His research has shown that labeling of negative emotions, also called can help people recover control. His fMRI brain scan research shows that labeling of emotions decreases activity in the brain’s emotional centers, including the amygdala. “affect labeling” And once your amygdala is calm—that part of your brain involved in mode—it gives you the chance to take a step back and come up with a more thoughtful response. “fight-flight-freeze” In the brain, naming an emotion can help calm it. Here is where finding words to label an internal experience becomes really helpful. We can call this “Name it to tame it.” And sometimes these low-road states can go beyond being unpleasant and confusing—they can even make life feel terrifying. If that is going on, talk about it. Your inner sea and your interpersonal relationships will all benefit from naming what is going on and bringing more integration into your life. — Daniel J. Siegel Instead of letting your unconscious brain exaggerate the negative effects of a temporary emotional setback making you feel powerless and helpless, recognizing and naming the emotion can give you a sense of control enabling you to choose a more appropriate response. Don’t Settle for Superficial Reasoning, Dig Deeper Connecting with the emotions and naming them gives you the mental clarity needed to think clearly. When your thinking is not clouded by emotions, you can face the facts of what really happened—what caused you to behave this way. By understanding what caused you to lose your calm, you can do better damage control and can also prevent it from happening again. Engaging in deep self-reflection can help you uncover the real reason behind your outburst as opposed to what initially appeared to have triggered it: What situation were you in when you had an emotional outburst? What about the situation you did not like—was it something someone said or did, was it the way you were treated, were you denied something that was important to you, were your beliefs and values challenged in some way? Was it the first time you felt this way or has it happened before? What did you say to yourself at that moment? What caused you to finally lose your calm—dig deeper into the real reason and not what appears to be the issue at first. For example: You may snap at a coworker after your manager tells you that you have been passed up for a promotion. You may yell at an intern after a recent conflict with a coworker who blamed you for delaying the project. In these situations, what you’re experiencing is unhandled frustration which leads to psychological displacement, a term coined by Sigmund Freud which is an unconscious defense mechanism in which your mind substitutes a new aim or a new object for goals felt in their original form to be dangerous or unacceptable. It operates in the mind unconsciously with transference of emotions, ideas, and wishes in the face of aggressive impulses. By ignoring whatever annoys you about work, you let the inner conflict bubble up inside. The stress and anxiety that comes from unhandled frustrations can cause you to overreact. Amy Arnsten, an American neuroscientist explains how stress can impair judgment “The release of high levels of catecholamines during uncontrollable stress rapidly switches the brain from a thoughtful, reflective state to a more unconscious, reflexive state. This may save our lives when we are in danger, but it is often unhelpful when we need top-down control to properly guide our actions and decisions.” Knowing how the environment is pulling your strings and playing you is critical to making responsive rather than reactive moves. — Ronald Heifetz By understanding your triggers, you can prevent your autopilot mode from kicking off destructive behaviors and instead deliberately engage in constructive actions. Instead of Berating Yourself, Engage in Self-Kindness An emotional reaction that you’re not proud of can turn you into a self-critic. You may think that being harsh and mean to yourself will set you right, but engaging in self-criticism at such moments is the worst thing you can do to yourself. Self-criticism involves a harsh and judgmental internal dialogue in response to perceived failures, mistakes, or shortcomings. It often includes negative self-talk, self-blame, and self-condemnation. Having a rigid and unforgiving attitude towards yourself can make you feel shameful, guilty, and inadequate. It can lead to increased stress, anxiety, diminished sense of self-worth, and a cycle of negativity that undermines self-confidence and well-being. Instead of engaging in self-criticism after an emotional outburst, try being self-compassionate. Self-compassion involves responding to yourself with kindness, understanding, and acceptance when faced with difficulties or when you fall short of your own expectations. Self-compassionate people take a more balanced and realistic view of their imperfections which fosters a sense of worthiness and self-acceptance. They understand that everyone makes mistakes; it is a natural part of being human. The biggest misconception about self-compassion is that it can undermine your motivation to push yourself to do better; being warm and kinder to yourself will make you lazy. Quite the opposite. Self-compassion is strongly associated with emotional resilience. Self-compassion does not mean avoiding responsibility for your actions or ignoring areas in which you need to improve. Instead, self-compassion involves approaching yourself with kindness and understanding, even when you make mistakes or face difficulties thereby fostering a healthier and more positive relationship with yourself. shows that being compassionate to oneself builds emotional resilience and psychological well-being. People who are self-compassionate: Research Have lower levels of depression and anxiety. Less likely to ruminate on negative thoughts and emotions or suppress them. Show increased optimism. Are able to take personal initiative. Involved with more positive emotions like kindness, happiness, and connectedness. For example, after an emotional outburst: Instead of: I can't believe I messed up. I'm so stupid. Say: It's okay to make mistakes; everyone does. I can be kind to myself and learn from this experience. The truth is this: sometimes we display good qualities and sometimes bad. Sometimes we act in helpful, productive ways and sometimes in harmful, maladaptive ways. But we are not defined by these qualities or behaviors. We are a verb not a noun, a process rather than a fixed “thing.” Our actions change—mercurial beings that we are—according to time, circumstance, mood, setting. — Kristin Neff Don’t Just Apologize; Own Your Reaction Finally, you have to face the people who experienced your emotional outburst. You can’t ignore them or pretend as if nothing happened. You may try to put the reaction behind you and just go on with your day with the assumption that everyone will forget about it, and things will get better on their own. But, not owning how you behaved makes things awkward for others. They may feel uncomfortable around you and hesitate to approach you or avoid communicating with you altogether. Not communicating what led to an emotional outburst has another problem—when others don’t know what made you lose your calm, they’re free to create their own version and treat it as a source of truth. She’s moody. She’s completely unpredictable. She is rude and mean. To avoid being labeled after an emotional outburst, own your reaction. To do this: Don’t just apologize, talk about what led to overreaction. When it’s something that you can’t talk about openly, share a general description e.g., personal issue, conflict at work, lack of understanding, etc. Doing this will help others see that you’re just another human with flaws and imperfections. Reassure them that you’ll do your best to avoid emotional drama and have productive conversations. Reassurance goes a long way in rebuilding broken trust. If it happened due to a problem that can be discussed with them, seek out their advice on what must change to prevent it from happening to you or others again. Bringing others into the equation shows them that no one is immune to such outbursts. For example: Instead of: I am sorry that I lost my temper yesterday and yelled in front of the team. Say: I am sorry for how I reacted yesterday. I reflected on my behavior later during the day and realized that even though I was angry due to a recent conflict at work, I shouldn’t have lashed out my frustration at you. My behavior was completely unjustified and I promise not to engage in such unproductive expressions. I hope we can move on and can continue collaborating together. Strength is about how you show up. It requires you to choose what energy and action you want to bring to a given situation. At its heart, Strength is about self-management. It’s not about controlling your emotions—it’s about honoring them and choosing what you do next. — Darcy Luoma Summary Negative emotions at work can sometimes be so intense that they take away your ability to think clearly. Emotional fog can make you lose your calm and respond to others in unproductive ways. Hiding away or trying to avoid facing the reality of what happened will not fix the problem; it will only make it worse. Instead of avoiding the emotions after an emotional outburst, come in touch with your real feelings. Acknowledge them without judging them or trying to change them. Next, describe exactly how you’re feeling by naming the emotion. This practice will calm you down and give you an opportunity to take a step back and think clearly. With a clear head, you’ll be better positioned to reflect on your behavior and identify the root cause of your emotional outburst and what really triggered it. Criticizing yourself for your overreaction is a bad strategy. Instead of fixing the situation, it will keep you locked in a cycle of negative thinking. Practice self-compassion in such moments by giving yourself warmth, kindness, and empathy. Finally, own your mistake. Showing you aren’t perfect and vulnerable much like other human beings will bring you closer to others and strengthen your relationships at work. This story was previously published . Follow me on or here for more stories. here LinkedIn

How to Recover From an Emotional Outburst At Work

কর্মক্ষেত্রে একটি মানসিক বিস্ফোরণ থেকে কীভাবে পুনরুদ্ধার করবেন

So erholen Sie sich von einem Gefühlsausbruch am Arbeitsplatz

Cómo recuperarse de un arrebato emocional en el trabajo

Comment se remettre d'une explosion émotionnelle au travail

कार्यस्थल पर भावनात्मक आवेग से कैसे उबरें

職場での感情の爆発から立ち直る方法

직장에서 감정이 폭발했을 때 회복하는 방법

Como se recuperar de uma explosão emocional no trabalho

Как оправиться от эмоционального всплеска на работе

İşyerinde Duygusal Bir Patlamadan Nasıl Kurtulabilirsiniz?

Làm thế nào để phục hồi sau cơn bộc phát cảm xúc tại nơi làm việc

如何从工作中的情绪爆发中恢复过来

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References 6. Conclusion and Future Work In this paper, we summarize the approaches used by the participants for the Memotion 3 task and analyze the results. Due to the multi-modal nature of the dataset, all teams use a pre-trained image and text embedding models. However, each team presents a novel model pipeline. The highest scores achieved in Task A, Task B and Task C of Memotion 3.0 are 34.41%, 79.77% and 59.82% respectively, which shows there is significant room for improvement. On analysis of the results and the mis-classified examples on the test set, we find that "Sarcasm" and "Humour" are difficult to identify, especially in code-mixed memes. While we address Hind-English code-mixed memes in this paper, future work could include exploring other languages/language pairs. A unified baseline model to analyze memes in multiple languages could also be an interesting possibility. References [1] R. Dawkins, The selfish gene, Granada Publishing Lim, 1979. [2] A. Marwick, Memes, Contexts 12 (2013) 12–13. [3] N. Akhther, Internet memes as form of cultural discourse: A rhetorical analysis on facebook, 2018. doi:10.31234/osf.io/sx6t7. [4] S. 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Aguilar, S. Kar, S. Pandey, S. PYKL, B. Gambäck, T. Chakraborty, T. Solorio, A. Das, SemEval-2020 task 9: Overview of sentiment analysis of code-mixed tweets, in: Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020. [37] B. R. Chakravarthi, et al., Findings of the shared task on offensive language identification in Tamil, Malayalam, and Kannada, in: Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, 2021. [38] B. R. Chakravarthi, V. Muralidaran, R. Priyadharshini, J. P. McCrae, Corpus creation for sentiment analysis in code-mixed tamil-english text, arXiv:2006.00206 (2020). [39] B. R. Chakravarthi, N. Jose, S. Suryawanshi, E. Sherly, J. P. McCrae, A sentiment analysis dataset for code-mixed malayalam-english, arXiv preprint arXiv:2006.00210 (2020). [40] A. Hande, R. Priyadharshini, B. R. Chakravarthi, KanCMD: Kannada CodeMixed dataset for sentiment analysis and offensive language detection, in: Workshop on Computational Modeling of People’s Opinions, Personality, and Emotion’s in Social Media, 2020. [41] S. Dowlagar, R. Mamidi, Graph convolutional networks with multi-headed attention for code-mixed sentiment analysis, in: Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, 2021, pp. 65–72. [42] J. Risch, A. Stoll, M. Ziegele, R. Krestel, hpidedis at germeval 2019: Offensive language identification using a german bert model., in: KONVENS, 2019. [43] D. Tula, P. Potluri, S. Ms, S. Doddapaneni, P. Sahu, R. Sukumaran, P. Patwa, Bitions@DravidianLangTech-EACL2021: Ensemble of multilingual language models with pseudo labeling for offence detection in Dravidian languages, in: Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, 2021. [44] D. Tula, M. S. Shreyas, V. Reddy, P. Sahu, S. 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Karatzas, Exploring hate speech detection in multimodal publications, 2019. arXiv:1910.03814. [50] B. Zadeh, et al., Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph, in: ACL, 2018. [51] D. Kiela, H. Firooz, A. Mohan, V. Goswami, A. Singh, P. Ringshia, D. Testuggine, The hateful memes challenge: Detecting hate speech in multimodal memes, Neurips (2020). [52] S. Suryawanshi, B. R. Chakravarthi, Findings of the shared task on troll meme classification in Tamil, in: Speech and Language Technologies for Dravidian Languages, 2021. [53] E. Hossain, O. Sharif, M. M. Hoque, MUTE: A multimodal dataset for detecting hateful memes, in: Proceedings of the 2nd AACL Student Research Workshop, 2022. [54] Z. Xie, L. Liu, Y. Wu, L. Zhong, L. Li, Learning text-image joint embedding for efficient cross-modal retrieval with deep feature engineering, ACM Transactions on Information Systems 40 (2021) 1–27. 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Zubiaga, NUAA-QMUL-AIIT at Memotion 3: Multi-modal fusion with squeeze-and-excitation for internet meme emotion analysis, in: Proceedings of De-Factify 2: Workshop on Multimodal Fact Checking and Hate Speech Detection, CEUR, 2023. [74] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, V. Stoyanov, Roberta: A robustly optimized bert pretraining approach (2019). [75] X. Zhai, J. Puigcerver, A. Kolesnikov, et al., A large-scale study of representation learning with the visual task adaptation benchmark, 2020. arXiv:1910.04867. [76] G. Ke, Q. Meng, T. Finley, et al., Lightgbm: A highly efficient gradient boosting decision tree, in: Neurips, 2017. [77] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: CVPR, 2016. [78] F. Schroff, D. Kalenichenko, J. Philbin, FaceNet: A unified embedding for face recognition and clustering, in: CVPR, 2015.

Overview of Memotion 3: Sentiment & Emotion
Analysis of Codemixed Hinglish - Conclusion

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References 5. Results The performance in Task A i.e. sentiment classification is presented in Table 1. Our proposed baseline achieved a score of 33.28%. Out of the five final submissions, four teams managed to surpass the baseline. The top two teams, namely, NUAA-QMUL-AIIT [73] and NYCU_TWO [71] improved on the baseline by 1.1% and 0.9% respectively. The difference in F1 scores for this task is minimal across all participants. Table 2 shows the leaderboard for Task B. Three teams out-performed the baseline for this task and top performing team wentaorub [68] improves on the final score by 5.4%. Team CUFE performs lower than the baseline score by 3.4%, however, they present the highest score on the Table 2 Leaderboard of teams on Task B: Emotion analysis {H:Humor, S:Sarcasm, O:Offense, M:Motivation}. All the scores are in percentage. The teams are ranked by their average F1 scores (overall) across all the four emotions. Motivation emotion is the easiest to detect while offense is the most difficult to detect. Offensive sub-task and on the Sarcasm sub-task, jointly with NYCU_TWO [71]. wentaorub [68], NYCU_TWO, and NUAA-QMUL-AIIT achieved the same score on Motivation, which is 4% higher than the baseline. NYCU_TWO also scored the highest in the Humour sub-task, improving on the baseline by 4.5%. Based on these F1 scores, we can deduce that the Motivation class is the easiest to detect, similar to Memotion 2.0 [59], despite some teams performing poorly on this class. However, in this iteration, the Offensive class is the hardest to detect, instead of the Sarcasm class. No single team performs the best on all the classes. Table 3 Leaderboard of teams on Task C: Emotion intensity detection {H:Humor, S:Sarcasm, O:Offense, M:Motivation}. All scores are in percentage. The teams are ranked by average F1 scores (overall) across all the four emotions. Intensity of sarcasm is by far the most difficult to detect. Motivation has only two intensities as opposed to four intensity levels for other emotions. As shown in the leaderboard for Task C in Table 3, all the participating teams outperform the baseline in this task. The minimum improvement in F1 score is 0.8% by team CUFE and the maximum improvement in the final score is 7.5% by wentaorub [68]. The submission by wentaorub exceeds all other teams in the sub-tasks Humour, Offensive and Motivation. NUAA-QMUL-AIIT [73] matched the highest score of wentaorub in the Motivation category, while CUFE achieved the highest score in the Sarcasm sub-task. The Motivation class has significantly higher F1 scores than other classes, due to it having only 2 intensities whereas other classes have 4 intensities each. The best overall score is only 59.82%, which shows the difficulty of the task. A major observation when comparing performances with the previous iteration of the task [7] is that the performance on Task A is much worse in Memotion 3. Overall Scores in Tasks B and C have remained about the same compared to Memotion 2. For each individual task, we analyze the memes from the test set that all participants made wrong predictions on. For Task A, there are 121 memes where all participants mis-classified the label, out of which 66 memes were true negative sentiment followed closely by true positive memes. For task B, there are 231 such memes, majority of which belong to "humor" and "sarcasm" class. Finally, for Task C, 421 memes are mis-classified by all the systems - most memes mis-classified by all teams are "Very Funny", "Very Sarcastic", "Slightly Offensive". Some such examples for Task A, B and C are shown in figures 1, 2, and 3 respectively. Further, we note that most of such difficult examples have code-mixed text. As for the overall performances, only two teams - NUAA-QMUL-AIIT and NYCU_TWO [73, 71] - perform better than the baseline in all tasks.

Overview of Memotion 3: Sentiment & Emotion
Analysis of Codemixed Hinglish -Results

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References 4. Participating systems There were 47 team registrations for the task in the Memotion 3.0 task page, of which 5 teams made submissions for the final test set of the dataset. The results for all three tasks are given in the following section and an overview of the 4 teams that presented their description papers are provided below. Table 1 Leaderboard of teams on Task A: Sentiment Analysis. [68] use CLIP [69] for individual text and image embeddings, before concatenating them and passing them through multi-headed attention layers for classification. They also use the OSCAR [70] model in this approach and an ensemble of their models are used for the final submission. Datasets such as Facebook Hateful memes [51], MMHS150k [49] etc. are used for pre-training. This architecture helped them achieve the best performance in Task B and C. wentaorub [71] propose a two model pipelines, namely Coopoerative Teaching Model (CTM) for task A and Cascaded Emotion Classifier (CEC) for task B and C. A fusion of multimodal embeddings from pre-trained Swin-Transformer [72] and CLIP are passed to the CTM and CEC pipelines. CEC helps leverage task C predictions for task B by jointly training the model. This team attained the best results in 3 out of the 4 labels in Task B. NYCU_TWO [73] refer to their approach as Squeeze-and-Excitation Fusion or SEFusion. The textual features from pre-trained RoBERTa [74] and visual features from CLIP-ViT [75] are fused to obtain multi-modal embeddings. The fusion is the SEFusion module, which uses a learned activation of the squeezed features, allowing for weighted fusion of multi-modal embeddings. This approach led to NUAA-QMUL-AIIT being the 1st ranked team in Task A. NUAA-QMUL-AIIT used a LightGBM [76] classifier for classification on every individual emotion or label in all Tasks. The inputs to the classifier were pre-trained Hinglish-DistilBERT for text embeddings and ResNet18 [77] for image embeddings. Other features, such as occurrence of characters, word count etc. from text and number of faces in the memes (extracted using Facenet’s [78] multitask cascaded CNN), were also used. CUFE obtained the highest score in 2 labels in Task B and 1 label in Task C. CUFE

Overview of Memotion 3: Sentiment & Emotion
Analysis of Codemixed Hinglish -Participating Systems

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References 3. Task Details 3.1. Tasks Memotion 3 is the 3rd iteration of the Memotion task. The challenge consists of three sub-tasks: 1. Task A - Sentiment analysis of memes: Given a meme, the system is supposed to classify the meme’s sentiment as positive, negative or neutral. 2. Task B - Overall emotion analysis of memes: This task’s goal is to pinpoint certain emotions connected to a given meme. Whether a meme is humorous, sarcastic, offensive, or motivating should be indicated by the system. There are multiple categories in which a meme can fit. 3. Task C - Classifying the intensity of meme emotions: The task is to determine the degree to which a particular emotion is being expressed. The ranking of these emotions is as follows: a) Humour: Not funny, funny, very funny and hilarious b) Sarcasm: Not Sarcastic, little sarcastic, very sarcastic and extremely sarcastic c) Offensive: Not offensive, slightly offensive, very offensive and hateful offensive d) Motivation: Not motivational, motivational 3.2. Dataset The tasks were conducted on the Memotion 3 dataset [5]. It consists of Hindi-English codemixed memes, which were collected from selenium based web crawler and they were gathered from various public platforms like Reddit, Google Images, etc and annotated manually. The dataset consists of 10,000 meme images divided into a train-val-test split of 7000-1500-1500. Each meme is annotated for its sentiment, emotion and intensity of emotion. Images also have their corresponding OCR text extracted with the help of Google Vision APIs and their respective URLs. For more details of the dataset, please refer to [5]. 3.3. Evaluation As mentioned previously, there are three tasks. Scoring is done for each task separately, and separate leaderboards are generated. For each task, we use weighted average F1 score to measure the performance of a model. The participants had access to only train and validation set. They were asked to submit a maximum of 3 submissions on the test set for each task, the best of which was selected as part of the leaderboard. 3.4. Baselines For multi-modal data, it is crucial to take into account both the visual and textual properties, particularly in the case of memes where the context can only be recorded using a mix of both elements. For textual features, we employ a multilingual form of BERT, namely HinglishBERT (BERT base-multilingual-cased) [66], which is tuned on Hinglish data. The trained Vision Transformer (ViT) model provides the visual properties [67]. The Hinglish-BERT embedding is concatenated with the pooled output from the ViT model. After passing through an MLP, the combined features are then categorised in a final classification layer. For more details about the baseline, please refer [5].

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