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   https://doi.org/10.18653/v1/2020.emnlp-main.369  

   Keung, Phillip; Lu, Yichao; Szarvas, György Szarvas; Smith, Noah A. (January 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID, and the coarse-grained product category (e.g., ‘books’, ‘appliances’, etc.) The corpus is balanced across the 5 possible star ratings, so each rating constitutes 20% of the reviews in each language. For each language, there are 200,000, 5,000, and 5,000 reviews in the training, development, and test sets, respectively. We report baseline results for supervised text classification and zero-shot cross-lingual transfer learning by fine-tuning a multilingual BERT model on reviews data. We propose the use of mean absolute error (MAE) instead of classification accuracy for this task, since MAE accounts for the ordinal nature of the ratings. 

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2. On Positivity Bias in Negative Reviews

   https://doi.org/10.18653/v1/2021.acl-short.39  

   Aithal, Madhusudhan; Tan, Chenhao (January 2021, Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers))
3. Linguistic Features for Detecting Fake Reviews

   https://doi.org/10.1109/ICMLA51294.2020.00063  

   Abri, Faranak; Gutierrez, Luis Felipe; Namin, Akbar Siami; Jones, Keith S.; Sears, David R. (December 2020, 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA))

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4. Ensemble Learning for Detecting Fake Reviews

   https://doi.org/10.1109/COMPSAC48688.2020.00-73  

   Gutierrez-Espinoza, Luis; Abri, Faranak; Siami Namin, Akbar; Jones, Keith S.; Sears, David R. (July 2020, 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC))

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5. Structured Neural Topic Models for Reviews

   Esmaeili, Babak; Huang, Hongyi; Wallace, Byron; van de Meent, Jan-Willem (January 2019, Proceedings of Machine Learning Research)

   We present Variational Aspect-based Latent Topic Allocation (VALTA), a family of autoencoding topic models that learn aspect-based representations of reviews. VALTA defines a user-item encoder that maps bag-of-words vectors for combined reviews associated with each paired user; item onto structured embeddings, which in turn define per-aspect topic weights. We model individual reviews in a structured manner by inferring an aspect assignment for each sentence in a given review, where the per-aspect topic weights obtained by the user-item encoder serve to define a mixture over topics, conditioned on the aspect. The result is an autoencoding neural topic model for reviews, which can be trained in a fully unsupervised manner to learn topics that are structured into aspects. Experimental evaluation on large number of datasets demonstrates that aspects are interpretable, yield higher coherence scores than non-structured autoencoding topic model variants,; can be utilized to perform aspect-based comparison; genre discovery. 

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