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Hypex: A Tool for Extracting Business Intelligence from Sentiment Analysis using Enhanced LSTM

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Abstract

Sentiment analysis, an application of machine learning in business is the process of identifying and cataloging comments, reviews, tweets, feedback, and even random rants according to the tone or sentiments conveyed by it. The data is analysed using machine learning approach of Long Short Term Memory (LSTM) rating the sentiments on a scale ranging from −100 to 100. A new proposed activation function is used for LSTM giving best results as compared to the existing Artificial Neural Network (ANN) techniques. Depending upon the mined opinion, the business intelligence tools evaluate the products or services of a company eventually resulting in the increase of the sales of that company. The results clearly show that BI extracted from SA is quite instrumental in driving business effectiveness and innovation.

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References

  1. Adventures of Don Quixote de la Mancha. (2018, August 08) url:https://play.google.com/store/books/details/Miguel_de_Cervantes_Saavedra_Adventures_of_Don_Qui?id=_6YZAAAAYAAJ&showAllReviews=true.

  2. Agarap AF, Grafilon P (2018) Statistical analysis on E-commerce reviews, with sentiment classification using bidirectional recurrent neural network (RNN). arXiv preprint arXiv:1805.03687

  3. Ain QT, Ali M, Riaz A, Noureen A, Kamran M, Hayat B, Rehman A (2017) Sentiment analysis using deep learning techniques: a review. Int J Adv Comput Sci Appl 8(6):424

    Google Scholar 

  4. Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(4):1165

    Article  Google Scholar 

  5. Customer Review (2018, August 06) Amazon.com reviews, customer review. url:https://www.amazon.com/gp/customer-reviews/R2VHSIW4YVZWRR/ ref=cm_cr_arp_d_rvw_ttl?ie=UTF8&ASIN=B01F3KV47E.

  6. Customer Review (2018, August 06) Amazon.com reviews, customer review. url:https://www.amazon.com/gp/customer-reviews/R3T81JTO3RJDJR/ ref=cm_cr_arp_d_rvw_ttl?ie=UTF8&ASIN=B009PN70AQ

  7. Customer Review (2018, August 06) Amazon.com reviews, customer review. url:https://www.amazon.com/gp/customer-reviews/R39PGHD1HK1CIR/ ref=cm_cr_arp_d_rvw_ttl?ie=UTF8&ASIN=B010TWAU2Q.

  8. Daassi-Gnaba H, Oussar Y (2015) External vs. internal svm-rfe: the svm-rfe method revisited and applied to emotion recognition. Neural Network World 25(1):75

    Article  Google Scholar 

  9. Dr. Batra's Hair Fall Control Shampoo, 200ml. (2018, June 25), Review url:https://www.amazon.in/Dr- Batras-Control-Shampoo-200ml/product-reviews/B01N290KTP/.

  10. Dr. Batra's Hair Fall Control Shampoo, 200ml. (2018, June 25), Review url:https://www.snapdeal.com/product/dr-batras-hair-fall- control/635480126010

  11. Dr. Batra's Hair Fall Control Shampoo,oil 200 ml. (2018, June 25), Review url:https://www.flipkart.com/dr-batra-s-hair-fall-control-shampoo-oil-200-ml/product-reviews/itmf3xfvcqyjfwzw?pid=SMPESY43MWZ397YQ.

  12. Ebadati OME, Mortazavi MT (2018) An efficient hybrid machine learning method for time series stock market forecasting. Neural Network World 28(1):41–55

    Article  Google Scholar 

  13. Ebrahimi M, Yazdavar AH, Sheth A (2017) Challenges of sentiment analysis for dynamic events. IEEE Intell Syst 32(5):70–75

    Article  Google Scholar 

  14. Fang X, Zhan J (2015) Sentiment analysis using product review data. Journal of Big Data 2(1):5

    Article  Google Scholar 

  15. Feyzbakhsh SA, Matsui M (1999, October) Adam-eve genetic algorithm as a function optimizer. In IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 99CH37028) (Vol. 1, pp. 613-624). IEEE

  16. Gers FA, Schmidhuber E (2001) LSTM recurrent networks learn simple context-free and context-sensitive languages. IEEE Trans Neural Netw 12(6):1333–1340

    Article  Google Scholar 

  17. Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with LSTM

  18. Gupta, S. (2018, August 06) Applications of sentiment analysis in business. Url: https://dzone.com/articles/applications-of-sentiment-analysis-in-business.

  19. He R, McAuley J (2016, April) Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web (pp. 507-517). International world wide web conferences steering committee

  20. Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6(02):107–116

    Article  Google Scholar 

  21. Huang JP, Wang XA, Zhao Y, Xin C, Xiang H (2018) Large earthquake magnitude prediction in Taiwan based on deep learning neural network. Neural Network World 28(2):149–160

    Article  Google Scholar 

  22. Ilayaraja S (2018, June 26) ilayaraja97/keras-contrib: Keras community contributions url:https://github.com/ilayaraja97/keras-contrib

  23. Jadav BM, Vaghela VB (2016) Sentiment analysis using support vector machine based on feature selection and semantic analysis. International Journal of Computer Applications, 146(13)

  24. Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011, June) Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies-volume 1 (pp. 142-150). Association for Computational Linguistics

  25. MajorLeagueJesus (2018, June 26) Rreview Counter-Strike: Global Offensive url:https://steamcommunity.com/id/MajorLeagueJesus/recommended/730/.

  26. Mautner P, Moucek R (2012) Processing and categorization of Czech written documents using neural networks. Neural Network World 22(1):53–66

    Article  Google Scholar 

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

  28. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111-3119)

  29. Pennington J, Socher R, Manning C (2014, October) Glove: global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543)

  30. Plaza L, de Albornoz JC (2012) Sentiment analysis in business intelligence: a survey. In Customer Relationship Management and the Social and Semantic Web: Enabling Cliens Conexus (pp. 231-252). IGI global

  31. Pulver A, Lyu S (2017, May) LSTM with working memory. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 845-851). IEEE

  32. Saggion H, Funk A (2009) Extracting opinions and facts for business intelligence. RNTI Journal, E (17), 119, 146

  33. Seibold M, Jacobs D, Kemper A (2012) Operational business intelligence: processing mixed workloads. It Professional 15(5):16–21

    Article  Google Scholar 

  34. Singh B, Kushwaha N, Vyas OP (2016, November) An interpretation of sentiment analysis for enrichment of business intelligence. In 2016 IEEE Region 10 Conference (TENCON) (pp. 18-23). IEEE

  35. Standard search API – Twitter (2018, June 28) Twitter, url:https://www.developer.twitter.com/en/docs/tweets/search/api-reference/get-search-tweets.

  36. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Computational linguistics 37(2):267–307

    Article  Google Scholar 

  37. The Homoeopathic Clinic by Dr. Riyanka Bhardwaj- Homeopathy Doctor in Greater Noida. (2018, August 08) url:https://www.google.com/search?q=The+Homoeopathic+Clinic+by+Dr.+Riyanka+Bhardwaj-+Homeopathy+Doctor+in+Greater+Noida+reviews.

  38. Xie Z (2012, October) A non-linear approximation of the sigmoid function based on FPGA. In 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) (pp. 221-223). IEEE

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Acknowledgments

The authors are sincerely thankful to the potential reviewers for their valuable comments and suggestions to improve the quality of the paper.

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Authors and Affiliations

  1. Indian Institute of Information Technology, Kota, India

    Ilayaraja Sreesurya & Himani Rathi

  2. Indian Institute of Information Technology, Nagpur, India

    Pooja Jain & Tapan Kumar Jain

Authors
  1. Ilayaraja Sreesurya
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  2. Himani Rathi
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  3. Pooja Jain
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  4. Tapan Kumar Jain
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Correspondence to Pooja Jain.

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Sreesurya, I., Rathi, H., Jain, P. et al. Hypex: A Tool for Extracting Business Intelligence from Sentiment Analysis using Enhanced LSTM. Multimed Tools Appl 79, 35641–35663 (2020). https://doi.org/10.1007/s11042-020-08930-6

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  • DOI: https://doi.org/10.1007/s11042-020-08930-6

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