DOI: https://doi.org/10.15587/2313-8416.2018.135069

Research of the methods of the analysis of reviews about the goods of electronics shops

Olekander Vechur, Oleksii Spodarets

Abstract


The research is devoted to the study of methods for analyzing reviews. The subject of research is the feedback on the goods. The aim of research is analysis of the NLP methods in the context of the task of reviewing feedback. The research method is computer and mathematical modeling.

Various classes of methods of the analysis reviews about the good are considered in the work, a comparison of the forecasting results is implemented. Research results can be applied for the analysis of reviews of any store


Keywords


natural language processing; computational algorithms; data analysis; computational linguistics

References


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Srivastava, S., Labutov, I., Mitchell, T. (2017). Joint concept learning and semantic parsing from natural language explanations. Empirical Methods in Natural Language Processing. Copenhagen, 1527–1536. doi: http://doi.org/10.18653/v1/d17-1161

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GOST Style Citations


Data programming: Creating large training sets, quickly / Ratner A. et. al. // Advances in Neural Information Processing Systems (NIPS). New York: Curran Associates, 2016. P. 3567–3575.

Lei T., Barzilay R., Jaakkola T. Rationalizing neural predictions // Empirical Methods in Natural Language Processing. Austin, 2016. P. 107–117. doi: http://doi.org/10.18653/v1/d16-1011 

Roth B., Klakow D. Combining generative and discriminative model scores for distant supervision // Empirical Methods in Natural Language Processing. Seattle, 2013. P. 24–29.

Srivastava S., Labutov I., Mitchell T. Joint concept learning and semantic parsing from natural language explanations // Empirical Methods in Natural Language Processing. Copenhagen, 2017. P. 1527–1536. doi: http://doi.org/10.18653/v1/d17-1161 

Voigt R., Jurafsky D. The Users Who Say 'Ni': Audience Identification in Chinese-language Restaurant Reviews // Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Beijing, 2015. P. 314–319. doi: http://doi.org/10.3115/v1/p15-2052 

88% Of Consumers Trust Online Reviews As Much As Personal Recommendations // Search Engine Land. URL: https://searchengineland.com/88-consumers-trust-online-reviews-much-personal-recommendations-195803 (Last accessed: 04.06.2018)

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank // Stanford University Sentiment Analysis. URL: https://nlp.stanford.edu/sentiment/ (Last accessed: 07.06.2018)

Wang S., Manning C. Baselines and Bigrams: Simple, Good Sentiment and Topic Classification // Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Jeju, 2012. P. 90–94.

Hochreiter S., Schmidhuber J. Long Short-Term Memory // Neural Computation. 1997. Vol. 9, Issue 8. P. 1735–1780. doi: http://doi.org/10.1162/neco.1997.9.8.1735 

Backpropagation Applied to Handwritten Zip Code Recognition / LeCun Y. et. al. // Neural Computation. 1989. Vol. 1, Issue 4. P. 541–551. doi: http://doi.org/10.1162/neco.1989.1.4.541 







Copyright (c) 2018 Olekander Vechur, Oleksii Spodarets

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ISSN 2313-8416 (Online), ISSN 2313-6286 (Print)