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Rough Start for Novogratz: Galaxy Digital Holdings Reports Net Loss of $272.7 Million in 2018

Author:
(1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es);
(2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es);
(3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Related work The proposed pipeline Evaluation Conclusion & Acknowledgements References & Appendix 3. The proposed pipeline The purpose of the proposed pipeline is, given the text reviews of an Amazon product, to classify them as normal if they refer to it, and as anomalous if they describe different products or if they are so generic that they do not provide useful information to consumers. These classifications will be accompanied by a normality score and explanations that justify them. Figure 1 shows the modules that are part of the proposed pipeline to solve this task. In the first phase, we encode the text reviews of the target product using the pretrained MPNet transformer [25]. In the second phase, a DAEF [26] autoencoder is trained using these embeddings. Using the reconstruction errors issued by the network and a predefined threshold error, we can classify reviews as normal (error greater than the threshold) or anomalous (error less than the threshold). For the last phase, we propose a method based on the most frequent normal terms to generate an explanation associated with each classification. Therefore, the proposed pipeline is composed of three main modules: (1) Text encoding; (2) Anomaly detection; (3) Explainability. The following sections describe each of them in detail. 3.1. Text encoding Although there are models capable of dealing directly with images or text, machine learning models are usually designed to be trained using numerical vectors that represent the sample data (tabular data). This input format of the data is the usual one for classic anomaly detection methods. In the NLP area, there are multiple techniques to represent texts using vectors of real numbers, which are known as embeddings. These techniques allow the generation of vector spaces that try to represent the relationships and semantic similarities of the language, so that, for example, two synonymous words will be found at a shorter distance in the vector space than two unrelated words. Embeddings can be calculated independently for each word of the language (word embeddings), which led to models such as word2vec [27] or Global Vectors (GloVe) [28]. The representation of a sentence (sentence embedding) or a document (document embedding) will therefore be the sum of all the individual representations of the terms that make it up. To obtain representations of fixed length, it is usual to perform operations such as the mean. In certain cases, these operations between embeddings can worsen or even invalidate the final embedding, so specific models have been developed capable of understanding and representing a text as a whole, instead of just encoding it word by word. Among these models are those based on transformers [29], such as BERT [30], XLNet [31], GPT-3 [32] or GPT-4 [33], which have been trained over large-scale datasets and can solve different tasks, including sentence embedding. Since it inherits the advantages of the BERT and XLNet models while overcoming their limitations, we will use the pre-trained MPNet [25] model to calculate the embeddings of the reviews. MPNet combines Masked Language Modeling (MLM) and Permuted Language Modeling (PLM) to predict token dependencies, using auxiliary position information as input to enable the model to view a complete sentence and reduce position differences. Models like GPT-3 or GPT-4 are more advanced, but in addition to not being open source, they demand much higher computational resources, unaffordable for a significant part of the scientific community, as is our particular case. MPNet maps sentences and paragraphs to a 768 dimensional dense vector space e ∈ R 768×1 , providing fast and quality encodings. Computing time is a critical aspect in the area in which this work is framed, since in e-commerce platforms (and online reviews in general) we can find a huge number of products and reviews to deal with. Therefore, the proposed pipeline uses MPNet to calculate the embeddings of the reviews, which allows them to be represented as numeric vectors of fixed size. This model and others are available in the Hugging Face repository [34]. 3.2. Anomaly detection Anomaly detection is a field with a large number of algorithms that solve the problem of distinguishing between normal and anomalous instances in a wide variety of ways [4, 35]. Depending on the assumptions made and the processes they employ, we can distinguish between five main types of methods: probabilistic, distance-based, information theory-based, boundarybased, and reconstruction-based methods. Among the latter are autoencoder networks (AE) [36], one of the most widely used models. AE are a type of self-associative neural network whose output layer seeks to reproduce the data presented to the input layer after having gone through a dimensional compression phase. In this way, they manage to obtain a representation of the input data in a space with a dimension smaller than the original, learning a compact representation of the data, retaining the important information, and compressing the redundant one. One of the requirements of our proposed pipeline is to generate a normality score for each review. This score will allow us, among other things, to order the different reviews by their level of normality. When AE networks are used in anomaly detection scenarios, the classification is usually carried out based on the reconstruction error that they emit to reproduce in its output the embeddings of the reviews it receives as inputs, which represents the level of normality of the evaluated instance. This reconstruction error can be used as the normality score we are looking for. Due to the speed of its training, we have decided to use DAEF (Deep Autoencoder for Federated learning) [26] as our anomaly detection model. Unlike traditional neural networks, DAEF trains a deep AE network in a non-iterative way, which drastically reduces its training time without losing the performance of traditional (iterative) AEs. The proposed pipeline uses DAEF to calculate the embeddings of the reviews, being able to issue a normality score associated with each of the review classifications. 3.3. Explainability As black-box ML models are mainly being employed to make important predictions in critical contexts, the demand for transparency is increasing. The danger is in creating and using decisions that are not justifiable, legitimate, or that do not allow obtaining detailed explanations of their behaviour. Explanations supporting the output of a model are crucial, moreover in fields such as medicine, autonomous vehicles, security, or finance. When ML models do not meet any of the criteria imposed to declare them explainable, a separate method must be applied to the model to explain its decisions. This is the purpose of post-hoc explainability techniques [37], which aim at communicating understandable information about how an already developed model produces its predictions for any given input. Within these techniques, the so-called model-agnostic techniques are those designed to be plugged into any model with the intent of extracting some information from its prediction procedure. To generate the explanations we have implemented an approach based on a statistical analysis of the dataset. Based on the definitions of normal and anomalous review presented in Section 2, Our hypothesis assumes that normal reviews will always refer directly or indirectly to the target product so that there will be a list of terms used very frequently among normal texts. The appearance of one or more of these “normal” terms in a review would justify its classification by the system as normal. In the same way, anomalous reviews may be justified with the non-presence of said terms. The original text reviews classified as normal using the AD model will be processed (tokenization, lemmatization and stopword removal) and analyzed to obtain the list of the top-n most frequent terms. This list of terms will be compared with the lists of frequent terms of other products in order to remove the terms they have in common, so that the final list for a product does not contain generic terms. This final list of terms will be used to generate the explanations. New reviews classified as normal will be searched for the presence of any of these terms. To make this search more flexible, it will also be analyzed if there are terms in the review semantically close to any of the words on the list. To quantify the closeness of two words, the cosine similarity emitted by the MPNet transformer model itself will be used as a measure of distance. Reviews classified as anomalous will be explained by the non-occurrence of terms from the list. Figure 2 represents an overall view of the pipeline considering the product “chocolate bars” as the normal class. In summary, in this work, we propose the use of this statistical analysis of the terms to generate the explanations associated with the classification of the reviews. This paper is available on arxiv under CC 4.0 license. Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Author: Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Abstract and Introduction Related work Related work The proposed pipeline The proposed pipeline Evaluation Evaluation Conclusion & Acknowledgements Conclusion & Acknowledgements References & Appendix References & Appendix 3. The proposed pipeline The purpose of the proposed pipeline is, given the text reviews of an Amazon product, to classify them as normal if they refer to it, and as anomalous if they describe different products or if they are so generic that they do not provide useful information to consumers. These classifications will be accompanied by a normality score and explanations that justify them. Figure 1 shows the modules that are part of the proposed pipeline to solve this task. In the first phase, we encode the text reviews of the target product using the pretrained MPNet transformer [25]. In the second phase, a DAEF [26] autoencoder is trained using these embeddings. Using the reconstruction errors issued by the network and a predefined threshold error, we can classify reviews as normal (error greater than the threshold) or anomalous (error less than the threshold). For the last phase, we propose a method based on the most frequent normal terms to generate an explanation associated with each classification. Therefore, the proposed pipeline is composed of three main modules: (1) Text encoding; (2) Anomaly detection; (3) Explainability. The following sections describe each of them in detail. 3.1. Text encoding 3.1. Text encoding Although there are models capable of dealing directly with images or text, machine learning models are usually designed to be trained using numerical vectors that represent the sample data (tabular data). This input format of the data is the usual one for classic anomaly detection methods. In the NLP area, there are multiple techniques to represent texts using vectors of real numbers, which are known as embeddings. These techniques allow the generation of vector spaces that try to represent the relationships and semantic similarities of the language, so that, for example, two synonymous words will be found at a shorter distance in the vector space than two unrelated words. Embeddings can be calculated independently for each word of the language (word embeddings), which led to models such as word2vec [27] or Global Vectors (GloVe) [28]. The representation of a sentence (sentence embedding) or a document (document embedding) will therefore be the sum of all the individual representations of the terms that make it up. To obtain representations of fixed length, it is usual to perform operations such as the mean. In certain cases, these operations between embeddings can worsen or even invalidate the final embedding, so specific models have been developed capable of understanding and representing a text as a whole, instead of just encoding it word by word. Among these models are those based on transformers [29], such as BERT [30], XLNet [31], GPT-3 [32] or GPT-4 [33], which have been trained over large-scale datasets and can solve different tasks, including sentence embedding. Since it inherits the advantages of the BERT and XLNet models while overcoming their limitations, we will use the pre-trained MPNet [25] model to calculate the embeddings of the reviews. MPNet combines Masked Language Modeling (MLM) and Permuted Language Modeling (PLM) to predict token dependencies, using auxiliary position information as input to enable the model to view a complete sentence and reduce position differences. Models like GPT-3 or GPT-4 are more advanced, but in addition to not being open source, they demand much higher computational resources, unaffordable for a significant part of the scientific community, as is our particular case. MPNet maps sentences and paragraphs to a 768 dimensional dense vector space e ∈ R 768×1 , providing fast and quality encodings. Computing time is a critical aspect in the area in which this work is framed, since in e-commerce platforms (and online reviews in general) we can find a huge number of products and reviews to deal with. Therefore, the proposed pipeline uses MPNet to calculate the embeddings of the reviews, which allows them to be represented as numeric vectors of fixed size. This model and others are available in the Hugging Face repository [34]. 3.2. Anomaly detection 3.2. Anomaly detection Anomaly detection is a field with a large number of algorithms that solve the problem of distinguishing between normal and anomalous instances in a wide variety of ways [4, 35]. Depending on the assumptions made and the processes they employ, we can distinguish between five main types of methods: probabilistic, distance-based, information theory-based, boundarybased, and reconstruction-based methods. Among the latter are autoencoder networks (AE) [36], one of the most widely used models. AE are a type of self-associative neural network whose output layer seeks to reproduce the data presented to the input layer after having gone through a dimensional compression phase. In this way, they manage to obtain a representation of the input data in a space with a dimension smaller than the original, learning a compact representation of the data, retaining the important information, and compressing the redundant one. One of the requirements of our proposed pipeline is to generate a normality score for each review. This score will allow us, among other things, to order the different reviews by their level of normality. When AE networks are used in anomaly detection scenarios, the classification is usually carried out based on the reconstruction error that they emit to reproduce in its output the embeddings of the reviews it receives as inputs, which represents the level of normality of the evaluated instance. This reconstruction error can be used as the normality score we are looking for. Due to the speed of its training, we have decided to use DAEF (Deep Autoencoder for Federated learning) [26] as our anomaly detection model. Unlike traditional neural networks, DAEF trains a deep AE network in a non-iterative way, which drastically reduces its training time without losing the performance of traditional (iterative) AEs. The proposed pipeline uses DAEF to calculate the embeddings of the reviews, being able to issue a normality score associated with each of the review classifications. 3.3. Explainability 3.3. Explainability As black-box ML models are mainly being employed to make important predictions in critical contexts, the demand for transparency is increasing. The danger is in creating and using decisions that are not justifiable, legitimate, or that do not allow obtaining detailed explanations of their behaviour. Explanations supporting the output of a model are crucial, moreover in fields such as medicine, autonomous vehicles, security, or finance. When ML models do not meet any of the criteria imposed to declare them explainable, a separate method must be applied to the model to explain its decisions. This is the purpose of post-hoc explainability techniques [37], which aim at communicating understandable information about how an already developed model produces its predictions for any given input. Within these techniques, the so-called model-agnostic techniques are those designed to be plugged into any model with the intent of extracting some information from its prediction procedure. To generate the explanations we have implemented an approach based on a statistical analysis of the dataset. Based on the definitions of normal and anomalous review presented in Section 2, Our hypothesis assumes that normal reviews will always refer directly or indirectly to the target product so that there will be a list of terms used very frequently among normal texts. The appearance of one or more of these “normal” terms in a review would justify its classification by the system as normal. In the same way, anomalous reviews may be justified with the non-presence of said terms. The original text reviews classified as normal using the AD model will be processed (tokenization, lemmatization and stopword removal) and analyzed to obtain the list of the top-n most frequent terms. This list of terms will be compared with the lists of frequent terms of other products in order to remove the terms they have in common, so that the final list for a product does not contain generic terms. This final list of terms will be used to generate the explanations. New reviews classified as normal will be searched for the presence of any of these terms. To make this search more flexible, it will also be analyzed if there are terms in the review semantically close to any of the words on the list. To quantify the closeness of two words, the cosine similarity emitted by the MPNet transformer model itself will be used as a measure of distance. Reviews classified as anomalous will be explained by the non-occurrence of terms from the list. Figure 2 represents an overall view of the pipeline considering the product “chocolate bars” as the normal class. In summary, in this work, we propose the use of this statistical analysis of the terms to generate the explanations associated with the classification of the reviews. 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

Explainable AI in Action: Generating Insights from Review Anomalies

Effective Anomaly Detection Pipeline for Amazon Reviews: References & Appendix

Author:
(1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es);
(2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es);
(3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Related work The proposed pipeline Evaluation Conclusion & Acknowledgements References & Appendix 5. Conclusion In this work, we have proposed a pipeline for detecting anomalous reviews associated with Amazon products, which can be directly extrapolated to other online review platforms or scenarios with similar characteristics. The representation of the reviews using MPNet embeddings has enabled the training of classical anomaly detection algorithms that have achieved a very good performance. These have been evaluated using reviews from different products and categories, and the score they emit allows us to sort the reviews based on their normality. A technique based on the occurrence of frequent terms has been proposed to generate explanations associated with the classifications of the reviews. This technique has been compared with SHAP, one of the reference post-hoc techniques in the field of explainability, and with GPT-3, due to its high power and versatility. To evaluate this aspect of the pipeline, we conducted a two-part survey in which 241 members of the university community participated. From the first part of the explainability test we can conclude that, in general terms, the effect of the explanations has not been beneficial for the users. In any case, these tests allow us to reflect on the difficulty of using explainability and evaluation techniques in borderline scenarios where subjectivity plays an important role, such as the one presented in this article or in other fields of NLP, as well as in areas such as image or audio generation. Regarding the second part of the explainability test, we have been able to conclude that respondents preferred explanations that presented a more natural and familiar appearance over more condensed and concise explanations such as those provided by SHAP, regardless of the explanation effect they provide. Explanations based on term frequency analysis have been preferred by respondents along with GPT-3, however, our approach presents a significantly lower computational costs and both its use and the explanations produced are simpler for the users. As future work, it would be interesting to evaluate GPT-3 or other large models carrying out the complete process followed by the pipeline proposed in this work, instead of being tested only in the explainability module. We have not carried out this test due to the high computational cost that would be involved in processing the thousands of reviews to be evaluated using GPT3. Another interesting possible line of future work would be to broaden the scope of the survey, both in terms of the number of products involved and the number of reviews, in order to clarify the conclusions reached at the forward simulation stage. Lastly, it would be very useful to try presenting the explanations issued by SHAP in a more familiar or natural format for the end user, so that we can see if their level of preference is increased for the general public. Acknowledgements This work was supported in part by grant Machine Learning on the Edge - Ayudas Fundación BBVA a Equipos de Investigación Científica 2019 ; the Spanish National Plan for Scientific and Technical Research and Innovation (PID2019-109238GB-C22 and TED2021-130599A-I00); the Xunta de Galicia (ED431C 2022/44) and ERDF funds. CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia, Spain through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). This paper is available on arxiv under CC 4.0 license. Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Author: Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Abstract and Introduction Related work Related work The proposed pipeline The proposed pipeline Evaluation Evaluation Conclusion & Acknowledgements Conclusion & Acknowledgements References & Appendix References & Appendix 5. Conclusion In this work, we have proposed a pipeline for detecting anomalous reviews associated with Amazon products, which can be directly extrapolated to other online review platforms or scenarios with similar characteristics. The representation of the reviews using MPNet embeddings has enabled the training of classical anomaly detection algorithms that have achieved a very good performance. These have been evaluated using reviews from different products and categories, and the score they emit allows us to sort the reviews based on their normality. A technique based on the occurrence of frequent terms has been proposed to generate explanations associated with the classifications of the reviews. This technique has been compared with SHAP, one of the reference post-hoc techniques in the field of explainability, and with GPT-3, due to its high power and versatility. To evaluate this aspect of the pipeline, we conducted a two-part survey in which 241 members of the university community participated. From the first part of the explainability test we can conclude that, in general terms, the effect of the explanations has not been beneficial for the users. In any case, these tests allow us to reflect on the difficulty of using explainability and evaluation techniques in borderline scenarios where subjectivity plays an important role, such as the one presented in this article or in other fields of NLP, as well as in areas such as image or audio generation. Regarding the second part of the explainability test, we have been able to conclude that respondents preferred explanations that presented a more natural and familiar appearance over more condensed and concise explanations such as those provided by SHAP, regardless of the explanation effect they provide. Explanations based on term frequency analysis have been preferred by respondents along with GPT-3, however, our approach presents a significantly lower computational costs and both its use and the explanations produced are simpler for the users. As future work, it would be interesting to evaluate GPT-3 or other large models carrying out the complete process followed by the pipeline proposed in this work, instead of being tested only in the explainability module. We have not carried out this test due to the high computational cost that would be involved in processing the thousands of reviews to be evaluated using GPT3. Another interesting possible line of future work would be to broaden the scope of the survey, both in terms of the number of products involved and the number of reviews, in order to clarify the conclusions reached at the forward simulation stage. Lastly, it would be very useful to try presenting the explanations issued by SHAP in a more familiar or natural format for the end user, so that we can see if their level of preference is increased for the general public. Acknowledgements This work was supported in part by grant Machine Learning on the Edge - Ayudas Fundación BBVA a Equipos de Investigación Científica 2019 ; the Spanish National Plan for Scientific and Technical Research and Innovation (PID2019-109238GB-C22 and TED2021-130599A-I00); the Xunta de Galicia (ED431C 2022/44) and ERDF funds. CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia, Spain through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). 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

Effective Anomaly Detection Pipeline for Amazon Reviews: Insights and Future Directions

Real-World Evaluation of Anomaly Detection Using Amazon Reviews

Author:
(1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es);
(2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es);
(3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Related work The proposed pipeline Evaluation Conclusion & Acknowledgements References & Appendix 2. Related work Anomaly detection has been a consolidated research field for years. Its great utility has allowed its techniques to be applied in numerous areas: medicine [9], industrial systems [10], electronic fraud [11], cybersecurity [12], etc. However, when we talk about texts and NLP, there is no massive application of these anomaly detection techniques as in the previous cases. This may be due to the difficulty in defining the idea of an anomaly in texts. Contrary to other scenarios, such as monitoring an industrial system through its sensors, in which the anomalous class will correspond to faults in the system, when the data is text, defining the concept of an anomaly is not trivial. One of the most important lines of research related to the detection of anomalies in texts is fake reviews detection, also known as spam review detection, fake opinion detection, and spam opinion detection [13]. The main problem associated with fake review detection is classifying the review as either fake or genuine. There are generally three types of fake reviews [14]: • Type 1 (untruthful opinions): Fake reviews describing users who post negative reviews to damage a product’s reputation or post positive reviews to promote it. These reviews are called fake or deceptive reviews, and they are difficult to detect simply by reading, as real and fake reviews are similar to each other. • Type 2 (reviews on brands only): Those that do not comment on the products themselves, but talk about the brands, manufacturers, or sellers of the products. Although they can be useful, they are sometimes considered spam because they are not targeted at specific products. • Type 3 (non-reviews): Non-reviews that are irrelevant and offer no genuine opinion. In the work carried out by J. Salminen et al. [15], the authors try to distinguish genuine reviews from fake reviews on Amazon. To have a labeled dataset of fake reviews, they use GPT-2 [16] to artificially generate them. After this, they solve the task of distinguishing between genuine and fake reviews by fine-tuning a pretrained RoBERTa [17] model. They also show that his model is also capable of detecting fake reviews manually written by humans. Ş. Öztürk Birim et al. [18] proposed to use relevant information as the review length, purchase verification, sentiment score, or topic distribution as features to represent costumers reviews. Based on these features, well-known machine learning classifiers like random forests (RF) [19] are applied for fake detection. In their article, D. U. Vidanagama et al. [20] incorporate review-related features such as linguistic features, Part-of-Speech (POS) features, and sentiment analysis features using a domain ontology to detect fake reviews with a rule-based classifier. L. Ruff et al. [21] presented Context Vector Data Description (CVDD), a text-specific anomaly detection method that allows working with sequences of variable-length embeddings using self-attention mechanisms. To overcome the limitations of CVDD, J. Mu et al. [22] proposed tadnet, a textual anomaly detection network that uses an adversarial training strategy to detect anomalous texts in Social Internet of Things. In addition, thanks to the capture of the different semantic contexts of the texts, both models achieve interpretability and flexibility, allowing to detect which parts of the texts have caused the anomaly. Other authors, instead of developing text-specific AD models, make use of well-known AD and NLP techniques to design architectures that solve the problem. B. Song et al. [23] propose to analyze the accident reports of a chemical processing plant to detect anomalous conditions. In this scenario, anomalous conditions are defined as unexperienced accidents that occur in unusual conditions. The authors work directly with the original text extracting the meaningful keywords of the reports using the term frequency-inverse document frequency (TF-IDF) index. Based on this, and using the local outlier factor (LOF) algorithm, they identify anomaly accidents in terms of local density clusters, finding four major types of anomaly accidents. Working with the original texts and not with embeddings they achieve a certain interpretability in the results. In the approach presented by S. Seo et al. [24], a framework for identifying unusual but noteworthy customer responses and extracting significant words and phrases is proposed. The authors use Doc2Vec to vectorize customer responses, then LOF is applied to identify unusual responses, and based on a TF-IDF analysis and the distances in the embedding space, they can visualize useful information about the results through a network graph. In the previous works and most cases, the detection of fake reviews focuses on detecting type 1 reviews, that is, reviews that positively or negatively describe a product but whose intention is not genuine since they do not come from a real buyer. Since there are several works based on this scenario [15, 18, 20], in this work we will focus on detecting type 2 and 3 reviews, which refer to reviews that do not provide information about the product itself. In this way, the objective of this work is to design a pipeline capable of distinguishing reviews related to a specific product (normal reviews) from reviews that do not and therefore do not provide information to the users that read them (anomalous reviews), for example, because they wrongly describe other products or because they are too generic. In addition to this, the classifications carried out by the system will be explained through an analysis process based on NLP techniques. This paper is available on arxiv under CC 4.0 license. Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Author: Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Abstract and Introduction Related work Related work The proposed pipeline The proposed pipeline Evaluation Evaluation Conclusion & Acknowledgements Conclusion & Acknowledgements References & Appendix References & Appendix 2. Related work Anomaly detection has been a consolidated research field for years. Its great utility has allowed its techniques to be applied in numerous areas: medicine [9], industrial systems [10], electronic fraud [11], cybersecurity [12], etc. However, when we talk about texts and NLP, there is no massive application of these anomaly detection techniques as in the previous cases. This may be due to the difficulty in defining the idea of an anomaly in texts. Contrary to other scenarios, such as monitoring an industrial system through its sensors, in which the anomalous class will correspond to faults in the system, when the data is text, defining the concept of an anomaly is not trivial. One of the most important lines of research related to the detection of anomalies in texts is fake reviews detection, also known as spam review detection, fake opinion detection, and spam opinion detection [13]. The main problem associated with fake review detection is classifying the review as either fake or genuine. There are generally three types of fake reviews [14]: • Type 1 (untruthful opinions): Fake reviews describing users who post negative reviews to damage a product’s reputation or post positive reviews to promote it. These reviews are called fake or deceptive reviews, and they are difficult to detect simply by reading, as real and fake reviews are similar to each other. • Type 2 (reviews on brands only): Those that do not comment on the products themselves, but talk about the brands, manufacturers, or sellers of the products. Although they can be useful, they are sometimes considered spam because they are not targeted at specific products. • Type 3 (non-reviews): Non-reviews that are irrelevant and offer no genuine opinion. In the work carried out by J. Salminen et al. [15], the authors try to distinguish genuine reviews from fake reviews on Amazon. To have a labeled dataset of fake reviews, they use GPT-2 [16] to artificially generate them. After this, they solve the task of distinguishing between genuine and fake reviews by fine-tuning a pretrained RoBERTa [17] model. They also show that his model is also capable of detecting fake reviews manually written by humans. Ş. Öztürk Birim et al. [18] proposed to use relevant information as the review length, purchase verification, sentiment score, or topic distribution as features to represent costumers reviews. Based on these features, well-known machine learning classifiers like random forests (RF) [19] are applied for fake detection. In their article, D. U. Vidanagama et al. [20] incorporate review-related features such as linguistic features, Part-of-Speech (POS) features, and sentiment analysis features using a domain ontology to detect fake reviews with a rule-based classifier. L. Ruff et al. [21] presented Context Vector Data Description (CVDD), a text-specific anomaly detection method that allows working with sequences of variable-length embeddings using self-attention mechanisms. To overcome the limitations of CVDD, J. Mu et al. [22] proposed tadnet, a textual anomaly detection network that uses an adversarial training strategy to detect anomalous texts in Social Internet of Things. In addition, thanks to the capture of the different semantic contexts of the texts, both models achieve interpretability and flexibility, allowing to detect which parts of the texts have caused the anomaly. Other authors, instead of developing text-specific AD models, make use of well-known AD and NLP techniques to design architectures that solve the problem. B. Song et al. [23] propose to analyze the accident reports of a chemical processing plant to detect anomalous conditions. In this scenario, anomalous conditions are defined as unexperienced accidents that occur in unusual conditions. The authors work directly with the original text extracting the meaningful keywords of the reports using the term frequency-inverse document frequency (TF-IDF) index. Based on this, and using the local outlier factor (LOF) algorithm, they identify anomaly accidents in terms of local density clusters, finding four major types of anomaly accidents. Working with the original texts and not with embeddings they achieve a certain interpretability in the results. In the approach presented by S. Seo et al. [24], a framework for identifying unusual but noteworthy customer responses and extracting significant words and phrases is proposed. The authors use Doc2Vec to vectorize customer responses, then LOF is applied to identify unusual responses, and based on a TF-IDF analysis and the distances in the embedding space, they can visualize useful information about the results through a network graph. In the previous works and most cases, the detection of fake reviews focuses on detecting type 1 reviews, that is, reviews that positively or negatively describe a product but whose intention is not genuine since they do not come from a real buyer. Since there are several works based on this scenario [15, 18, 20], in this work we will focus on detecting type 2 and 3 reviews, which refer to reviews that do not provide information about the product itself. In this way, the objective of this work is to design a pipeline capable of distinguishing reviews related to a specific product (normal reviews) from reviews that do not and therefore do not provide information to the users that read them (anomalous reviews), for example, because they wrongly describe other products or because they are too generic. In addition to this, the classifications carried out by the system will be explained through an analysis process based on NLP techniques. 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

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