43 Stories To Learn About Sentiment Analysis

Let's learn about Sentiment Analysis via these 43 free stories. They are ordered by most time reading created on HackerNoon. Visit the /Learn Repo to find the most read stories about any technology.

1. 14 Open Datasets for Text Classification in Machine Learning

Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights.

2. Why You Shouldn't Use a Database as Your App’s Data Source

Most apps and APIs use databases as their data source and that is for a great reason. Databases have been designed to be scalable, resilient, and fully featured to support many types of use cases and scenarios.

3. The Importance of Social Media Analytics

Nowadays, brands have discovered that social media is an indispensable cog in their golden wheel of success. As such, the activities that take place on every social media platform needs to be kept in check by these brands to maximize profit. Social media analytics helps you keep tabs on the progress of your marketing strategy as well as the welfare of your clients and competitors.

4. How Machine Learning is changing Financial Services in 2020

An Introduction to Machine Learning for Finance.

5. Sentiment Analysis using AWS Comprehend

Sentiment analysis uses AI to identify the core emotion behind a piece of text. In this article, we will look at how to build a sentiment analyzer using AWS Comprehend.

6. Importance of Sentiment Analysis as a Key Marketing Tool

Sentiment Analytics can help your marketing team to understand the sentiment of your target audience and identify any potential issues or concerns.

7. Thrilled to be Recognized as #1 Startup in Budapest

We’re really proud that we can be in a group of like-minded technologists and be acknowledged by them.

8. Introducing aasaan.ai: No-Code Yelp Sentiment Classification

Introduction

9. Analyzing Sentiment Of Tweets Is Really Easy If You Follow This Tutorial

Hello, Guys,

10. Explaining the Idea of Mood Blogging

Mood Blogging is primarily location based blogging that captures & enhances spirit of blogging.

11. Using Sentiment Analysis to Attain and Retain Customers

Analyzing customer sentiment allows businesses to look into how customers feel about their products & services.

12. Say Goodbye to Surveys and Hello to Sentiment Analysis

For the past several decades, surveys have been the main method for a business to gain insights on how customers feel about their products.

13. Text Classification in iOS using tensorflowlite [A How-To Guide]

Text classification is task of categorising text according to its content. It is the fundamental problem in the field of Natural Language Processing(NLP). More general applications of text classifications are in email spam detection, sentiment analysis and topic labelling etc.

14. Affective Computing, Mindless AI and a World for the Taking

Affective computing is a term that refers to the synergy between AI and psychology in order to understand and affect emotions. Another term for it could be emot

15. All You Need to Know about Sentiment Analysis in Power BI

Since most of the world is online, everything by consumers nowadays is being shared online, whether a bad experience or a good one.

16. 5 Questions to Ask Before Choosing An Online Brand Reputation Management Tool

If Sherlock Holmes were to do an investigation in the digital world, then an ORM - Online Reputation Management tool would be his best buddy. In the words of the inscrutable Holmes, it’s best to never trust impressions but concentrate on the details.

17. 4 Social Media Data Mining Techniques to Help Grow Your Online Business

Social media data mining has become a must-have strategy for understanding current trends, culture, and online business. This is because the world of social media is a thriving, ever-growing ocean of data, where hundreds of millions of tweets, instagram posts, and blog articles are published every day.

18. Unconventional Sentiment Analysis: BERT vs. Catboost

Unconventional sentiment analysis with CatBoost. The result is comparable to BERT SOTA.

19. Fintech: The Main Attractions of This Bustling Industry

Financial technology, or Fintech for short is a relatively avoided topic among tech enthusiasts, developers, programmers and etc. The reason is very simple actually. Developers don’t necessarily refer to their software as Fintech even though it’s quite literally associated with the financial industry.

20. Hacking Emotions: Interpreting Current Events through News Sentiment

How can organizations best measure news sentiment to gain insights about customer and investor behavior?

21. How to Use Streamlit and Python to Build a Data Science App

Web apps are still useful tools for data scientists to present their data science projects to the users. Since we may not have web development skills, we can use open-source python libraries like Streamlit to easily develop web apps in a short time.

22. Natural Language Processing: Explaining BERT to Business People

<TLDR> BERT is certainly a significant step forward in the context of NLP. Business activities such as topic detection and sentiment analysis will be much easier to create and execute, and the results much more accurate. But how did you get to BERT, and how exactly does the model work? Why is it so powerful? Last but not least, what benefits it can bring to the business, and our decision to integrate it into the sandsiv+ Customer Experience platform.</TLDR>

23. How to Perform Sentiment Analysis with Amazon Comprehend

How to analyze the sentiments from a text using AWS services like Amazon Comprehend, AWS IAM, AWS Lambda, and Amazon S3.

24. How To Build An n8n Workflow To Manage Different Databases and Scheduling Workflows

Learn how to build an n8n workflow that processes text, stores data in two databases, and sends messages to Slack.

25. We Built a Crypto Trading Algo Based on AI Sentiment Analysis

Introducing an AI-powered Algo trading platform on the blockchain.

26. 5 Must-Read Research Papers on Sentiment Analysis for Data Scientists

From virtual assistants to content moderation, sentiment analysis has a wide range of use cases. AI models that can recognize emotion and opinion have a myriad of applications in numerous industries. Therefore, there is a large growing interest in the creation of emotionally intelligent machines.

27. How AI Has Enhanced Sentiment Analysis Using Product Review Data

Customer feedback is great. But have you been able to turn that feedback into meaningful customer insights? A few years back, brands depended on surveys to gauge customers’ feelings about how their products were performing.

28. Intro to AI Analytics and Top 5 Use Cases for Businesses

Analytics works by extracting meaningful patterns in data and interpreting and communicating them.

29. 5 Real Ways to Start Implementing AI in your Ecommerce Stores

The implementation of AI in ecommerce should come as no surprise. Online businesses have always been quick to adopt new technologies, and this is how the industry thrives; enhancing the customer experience, discovering new markets, and driving further sales. And with the continued development of AI technology like chatbots, visual search, and personalized recommendations, the world of ecommerce is transforming again.

30. Naive Sentiment Analysis Using R

Cleuton Sampaio, October 2019

31. Your Guide to Natural Language Processing (NLP)

Everything we express (either verbally or in written) carries huge amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and value extracted from it. In theory, we can understand and even predict human behaviour using that information.

32. 50k+ News Story Study Finds Trump Gets 5x the Coverage, but Not Much Love

Negative sentiment around Trump lifted slightly when he was diagnosed — and dropped when he returned to the White House.

33. How Far Are We From a Real World Jarvis?

A Brief History of NLP Applications in the 21st Century

34. Twitter Sentiment Analysis for the 2019 Lok Sabha Elections

Introduction

35. A Guide to Sentiment Analysis APIs for SaaS Product Managers

A sentiment analysis API can be instrumental in helping you ace business strategy for growth. Check out the guide to sentiment analysis APIs for SaaS Managers

36. Text Processing and Sentiment Analysis of Twitter Data

A complete guide to text processing using Twitter data and R.

37. AI-driven Sentiment Analysis: Hacking Emotions to Boost Customer Service

“People will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

38. What Soggy Breadsticks Taught Us About Service and Social Listening

Breadsticks, of all things, were the reason for a huge backlash at the famous Olive Garden chain of restaurants both from customers and employees.

39. Text Classification Simplified with Facebook’s FastText

A step by step tutorial to analyse sentiment of Amazon product reviews with the FastText API

40. Sentiment Classification for 2019 Lok Sabha Elections Using Text Based Classifiers

Introduction

41. Blockchain-Verified Sentiment

The explosion of content on the world wide web, social media and chat networks greatly increased the interest in sentiment analysis from a growing number and variety of interested parties.

42. Sentiment Analysis with Python and AssemblyAI’s Speech Recognition API

If you’ve never heard of Sentiment Analysis, I hadn’t either before I stumbled on it in the documentation. That’s why I thought it would be interesting to try.

43. How Machines Learn Emotions: Sentiment Analysis of Amazon Product Reviews

How do you train machines to identify emotions? This is a tutorial for sentiment analysis of Amazon product reviews using machine learning algorithms.

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