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Customer Reviews’ Sentiments Analysis using Deep Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 30 |
| Year of Publication: 2020 |
| Authors: Kusum Mehta, Supriya P. Panda |
10.5120/ijca2020920842
|
Kusum Mehta, Supriya P. Panda . Customer Reviews’ Sentiments Analysis using Deep Learning. International Journal of Computer Applications. 175, 30 ( Nov 2020), 27-31. DOI=10.5120/ijca2020920842
Abstract
In this era of digitalization, Sentiment Analysis(SA) has become a necessity for progress and prosperity in marketing. Sentiment analysis has become a powerful way of knowing the opinions and thoughts of users. The viewpoint of the consumer, such as knowledge sharing would include a lot of useful experience, while one wrong idea will cost too much for the company.SA has many social media data-related problems, such as natural language interpretation, etc. Issue of theory and technique also affect the accuracy of detecting the polarity. There is a problem of text classification such as analysis of sentiments in document level, sentence level, feature based. Document level analysis is done by two approaches: supervised learning and unsupervised learning. Sentence level contains sentences containing opinions. Aspect based analysis have different attributes is performed in customer reviews. Product opinion is taken for knowing the sentiments. Opinions are compared and are extracted as a feature.SA is very important for business purpose because it gives the way for improving their operations and the products they offer. For improving business strategy it plays an important role. It provides many key benefits like impactful decisions, for finding relevant products, improving business strategy, beating competitions, tackling positive or negative issues affecting the product. To overcome such issues, deep learning processes are applied. The work focuses on two main tasks. Firstly, to extract sentiments present in data of social media of customers' reviews and secondly, to use the deep-learning process for the sentiments' extraction for customer reviews.
References
- Hussein, D.M.E.D.M., 2018. A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), pp.330-338.
- Strapparava, C. and Mihalcea, R., 2008, March.Learning to identify emotions in text.In Proceedings of the 2008 ACM symposium on Applied computing (pp. 1556-1560).
- Jeong, B., Yoon, J. and Lee, J.M., 2019. Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management, 48, pp.280-290.
- Tang, D., Qin, B. and Liu, T., 2015. Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(6), pp.292-303.
- Torao, Y., Naruki, S., Kaori, Y. and Masahiro, N., 1997. An emotion processing system based on fuzzy inference and subjective observations. Information sciences, 101(3-4), pp.217-247.
- Zhang, Y., Du, J., Wang, Z., Zhang, J. and Tu, Y., 2018, November. Attention based fully convolutional network for speech emotion recognition, In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 1771-1775). IEEE.
- Sharef, N.M., Zin, H.M. and Nadali, S., 2016.Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data. J. Comput. Sci., 12(3), pp.153-168.
- Liu, M., Li, S., Shan, S., Wang, R. and Chen, X., 2014, November. Deeply learning deformable facial action parts model for dynamic expression analysis,In Asian conference on computer vision ,pp. 143-157,Springer, Cham
- Strapparava, C. and Valitutti, A., 2004, May. Wordnet affect: an affective extension of wordnet. In ,Lrec(Vol. 4, No. 1083-1086, p. 40)
- Jain, V.K., Kumar, S., Jain, N. andVerma, P., 2016, May. A novel approach to track public emotions related to epidemics in multilingual data.In 2nd International Conference and Youth School Information Technology and Nanotechnology (ITNT 2016),Russia ,pp. 883-889
- Jain, V.K., Kumar, S. and Fernandes,S.L.,2017. Extraction of emotions from multilingual text using intelligent text processing and computational linguistics. Journal of computational science, 21, pp.316-326.
- Jain, V.K. and Kumar, S., 2020.Predictive analysis of emotions for improving customer services. In Natural Language Processing: Concepts, Methodologies, Tools,and Applications (pp. 808-817). IGI Global.
- Mikolov, T., Chen, K., Corrado, G. and Dean, J., 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
- Paredes-Valverde, M.A., Colomo- Palacios, R., Salas-Zárate, M.D.P. and Valencia-García, R., 2017. Sentiment analysis in Spanish for improvement of products and services: A deep learning approach. Scientific Programming, 2017
- Alharbi, A.S.M. and de Doncker, E.,2019. Twitter sentiment analysis with a deepneural network: An enhanced approach usinguser behavioral information. Cognitive Systems Research, 54, pp.50-61
- Pham, D.H. and Le, A.C., 2018. Learning multiple layers of knowledge representation for aspect based sentiment analysis. Data & Knowledge Engineering, 114, pp.26-39
- Preethi, G., Krishna, P.V., Obaidat, M.S., Saritha, V. and Yenduri, S., 2017, July. Application of deep learning to sentiment analysis for recommender system on cloud. In 2017,International Conference on Computer, Information and Tecommunication Systems (CITS) (pp. 93-97). IEEE
- Vateekul, P. and Koomsubha, T., 2016, July. A study of sentiment analysis using deep learning techniques on Thai Twitter data. In 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 1-6). IEEE
- Salas-Zárate,M.D.P., Medina-Moreira, J., Lagos-Ortiz, K., Luna-Aveiga, H., Rodriguez-Garcia, M.A. and Valencia-Garcia, R., 2017. Sentiment analysis on tweets about diabetes: an aspect-level approach. Computational and mathematical methods in medicine, 2017.
- Satapathy, R., Cambria, E. and Hussain, A., 2017. Sentiment Analysis in the Bio-Medical Domain. Springer: Berlin,Germa.ny.
- Chorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K. andBengio, Y., 2015. Attention-based models for speech recognition.In Advances in neural information processing systems(pp 577-585)
- Ciregan, D., Meier, U. and Schmidhuber, J., 2012, June. Multi-column deep neural networks for image classification.In 2012 IEEE conference on computer vision and pattern recognition (pp. 3642-3649).IEEE.
- Da Silva, I.N., Spatti, D.H., Flauzino, R.A., Liboni, L.H.B. and dos Reis Alves, S.F., 2017.Artificial neural network architecturesture and training processes.In Artificial neural networks (pp.21- 21-28).Springer, Cham.
- Ghazi, D., Inkpen, D. and Szpakowicz, S., 2010, June. Hierarchical versus flat classification of emotionsin intext.In Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text (pp. 140-146).
- Deriu, J.M. and Cieliebak, M., 2016. Sentiment analysis usingsing convolutional neural networks with multi-task training and distant supervision on italian tweets. In Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian,Napoli,Italy 5-7 December 2016. Italian Journal of Computational Linguistics.
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