A Novel Approach for Sentiment Classification on
DOI:
https://doi.org/10.61841/29kaq347Keywords:
Sentiment Analysis, Sentiment Classification, NaturalLanguage ProcessingAbstract
Sentiment analysis is the energetic region of research that focuses on analyzing the opinions or feelings of customers and classifying them into high-quality or bad reviews. In this paper, we endorse a new strategy for sentiment classification that critiques the usage of the map minimize concept. As we are conscious that in this technology of massive data, tremendous data/reviews are gathered through social media web sites at different areas that are distributed. Existing structures of Indian railways don’t classify and analyze the evaluations into superb and negative sentiments. Also, there is no automated classification of departments based upon the complaints or reviews acquired for further action. We address this trouble by creating a novel approach for sentiment classification using the map limit framework.
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1. Duyu Tang, Bing Qin, Furu Wei, Li Dong, Ting Liu, and Ming Zhou, "AJoint Segmentation and Classification Framework for Sentence LeveSentiment Classification", IEEE/ACM TRANSACTIONS ON AUDIOSPEECH AND LANGUAGE PROCESSING, VOL. 23, NO. 11, pp. 1750-1761, NOVEMBER 2015
2. Youfang Lin, Huaiyu Wan, Rui Jiang, Zhihao Wu, and Xuguang Jia, Inferring the Travel Purposes of Passenger Groups for Better Understanding of Passengers," IEEE Transactions on Intelligent Two Transportation Systems, vol. 16, no. 1, pp. 235-243, February 2015.
3. Evelien van der Hurk, Leo Kroon, Gábor Maróti, and Peter Vervest, Deduction of Passengers’ Route Choices From Smart Card Data," IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 1, pp. 430-440, February 2015.
4. Vo Ngoc Phu, Nguyen Duy Dat, Vo Thi Ngoc Tran, Vo Thi Ngoc Chau, and Tuan A. Nguyen, "Fuzzy Cmeans for English Sentiment Classification in an Adistributed System," Applied Intelligence, pp. 11-22, 05 November 2016.
5. Mohammad Salehan and Dan J. Kim, "Predicting the Performance of Online Consumer Reviews: A Sentiment Mining Approach to Big Data Analytics," Decision Support Systems, vol. 81, pp. 30-40, January 2016.
6. Farman Ali, Daehan Kwak, Pervez Khan, S.M. Riazul Islam, Kye HyuKim, and K.S. Kwak, "Fuzzy ontology-based sentiment evaluation of transportation and town feature critiques for safe traveling," Transportation Research Part C: Emerging Technologies, vol. 77, pp. 33-48, April 2017.
7. Tao Chen, Ruifeng Xu, Yulan He, and Xuan Wang, "Improving sentimenanalysis by means of sentence kind classification: the usage of BiLSTM-CRF and CNN,Expert Systems with Applications, vol. 72, pp. 221-230, 15 April 2017.
8. Jinyan Li, Simon Fong, Yan Zhuang, and Richard Khoury, "Hierarchical classification in text mining for sentiment evaluation of on-line news," SoftComputing, vol. 20, no. 9, pp. 3411–3420, September 2016.
9. Cagatay Catal and Mehmet Nangir, "A sentiment classification mode based on more than one classifier," Applied Soft Computing, vol. 50, pp. 135-141, January 2017.
10. Chihli Hung and Hao-Kai Lin, "Using Objective Words in SentiWordNet to Improve Word-of-Mouth Sentiment Classification," IEEE IntelligentSystems, vol. 28, no. 2, pp. 47-54, 2013.
11. M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-based methods for sentiment analysis,” Computer Linguist, vol. 37, no. 2, pp. 267–307, 2011.
12. Ali, F., Kim, E.K., Kim, Y.G., “Fuzzy ontology-based opinion mining and information extraction: a concept to automate the hotel reservation system," Applied Intelligence, vol. 42, no. 3, pp. 481–500, 2015.
13. C. Havasi, E. Cambria, B. Schuller, B. Liu, and H. Wang, “Knowledge-based approaches to concept-level sentiment analysis,” IEEE IntelligentSystem, vol. 28, no. 2, pp. 0012–14, Mar.-Apr. 2013.
14. C. D. Manning and H. Schütze, "Foundations of Statistical Natural Language Processing," Cambridge, MA, USA: MIT Press, 1999.
15. Xia, R., Zong, C., and Li, S. (2011), “Ensemble of feature sets and classification algorithms for sentiment classification," Information Science vol. 181, no. 6, pp. 1138-1152, March 2011.
16. Liu, B. (2012), “Sentiment analysis and opinion mining," Morgan Claypool.
17. Quan, C., Ren, F., “Unsupervised product characteristic extraction for feature-oriented opinion determination," Information Sciences, 272, pp. 16-28.
18. B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: Sentimentclassification the usage of computing device getting to know techniques,” In Proceedings of thEMNLP, pp. 79–86, 2002.
19. J. Zhao, L. Dong, J. Wu, and K. Xu, “Moodlens: An emoticon-based sentiment evaluation gadget for Chinese tweets,” In Proceedings of the SIGKDD, 2012.
20. Yang Y, Pedersen JO, “A comparative study on function resolution in text categorization," In Proceedings of the ICML’97, pp. 412–420, 1997.
21. A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, Learning phrase vectors for sentiment analysis,” In Proceedings of the ACL, 2011.
22. Vijay Mahadeo Mane, D.V. Jadhav, "Holoentropy-enabled decision tree for automated classification of diabetic retinopathy: the use of retinal funduimages, Biomedical Engineering / Biomedizinische Technik, 2016. B. Rajakumar, "The Lion's Algorithm: A New Nature-Inspired Search Algorithm," Procedia, vol. 6, pp. 126–135, 2012.
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