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Sentimental Analysis using Product Review Data


Affiliations
1 Assistant Professor, Sharda University, Greater Noida, Uttar Pradesh,, India
2 Assistant Professor, Sharda University, Uttar Pradesh, Greater Noida,, India
3 Student, Sharda University, Greater Noida, Uttar Pradesh,, India
4 Sharda University, Greater Noida, Uttar Pradesh,, India
     

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Our work systematically analyze the sentiment of product reviews and evaluate the correlation with their corresponding ratings.Sentiment analysis identifies the positive or negative mood represented in a piece of literature. Consumers write reviews withprecise ratings on e-commerce platforms such as Amazon. We’ve noticed that there are occasionally discrepancies between thereview and the rating. We performed deep learning guided sentiment analysis to identify such mismatches from amazon productreview data. We convert reviews to vectors using paragraph vector and use them to develop a neural network using a GRU orgated recurrent unit our perspective makes advantage of both the semantic link between review content and product information.

Keywords

Sentiment Analysis, RNN, SVM, GRU.
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  • Chen, T., Xu, R., He, Y., Xia, Y., & Wang, X. (2016). Learning user and product distributed representations using a sequence model for sentiment analysis. IEEE Computational Intelligence Magazine, 11(3), 34-44.

  • Johnson, R., & Zhang, T. (2014). Effective use of word order for text categorization with convolutional neural networks. arXiv preprint arXiv:1412.1058.

  • Kumar, J. A., & Abirami, S. (2015). An experimental study of feature extraction techniques in opinion mining. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 4(1), 15-21.

  • Le, Q., & Mikolov, T. (2014, June). Distributed representations of sentences and documents. In International Conference on Machine Learning (pp. 1188-1196). PMLR.

  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems.

  • Ouyang, X., Zhou, P., Li, C. H., & Liu, L. (2015, October). Sentiment analysis using convolutional neural network. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (pp. 2359-2364). IEEE.

  • Sharma, K., & Lin, K. I. (2013, April). Review spam detector with rating consistency check. In Proceedings of the 51st ACM Southeast Conference (pp. 1-6).

  • Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013, October). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1631-1642).

  • Tang, D., Qin, B., & Liu, T. (2015, July). Learning semantic representations of users and products for document level sentiment classification. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (volume 1: long papers, pp. 1014-1023).

  • Tang, D., Qin, B., Liu, T., & Yang, Y. (2015, June). User modeling with neural network for review rating prediction. In Twenty-Fourth International Joint Conference on Artificial Intelligence.

  • Vieira, J. P. A., & Moura, R. S. (2017, September). An analysis of convolutional neural networks for sentence classification. In 2017 XLIII Latin American Computer Conference (CLEI) (pp. 1-5). IEEE.

  • Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1), 1-14.

  • Bengio, Y., Ducharme, R., & Vincent, P. (2000). A neural probabilistic language model. Advances in Neural Information Processing Systems, 13.

  • Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.


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  • Sentimental Analysis using Product Review Data

Abstract Views: 290  |  PDF Views: 0

Authors

Amit Kumar
Assistant Professor, Sharda University, Greater Noida, Uttar Pradesh,, India
Sonia Setia
Assistant Professor, Sharda University, Uttar Pradesh, Greater Noida,, India
Arjun Singh
Student, Sharda University, Greater Noida, Uttar Pradesh,, India
Thomas Abraham
Sharda University, Greater Noida, Uttar Pradesh,, India
Yashaswi Shakya
Sharda University, Greater Noida, Uttar Pradesh,, India

Abstract


Our work systematically analyze the sentiment of product reviews and evaluate the correlation with their corresponding ratings.Sentiment analysis identifies the positive or negative mood represented in a piece of literature. Consumers write reviews withprecise ratings on e-commerce platforms such as Amazon. We’ve noticed that there are occasionally discrepancies between thereview and the rating. We performed deep learning guided sentiment analysis to identify such mismatches from amazon productreview data. We convert reviews to vectors using paragraph vector and use them to develop a neural network using a GRU orgated recurrent unit our perspective makes advantage of both the semantic link between review content and product information.

Keywords


Sentiment Analysis, RNN, SVM, GRU.

References