Sentiment Analysis about Smart Phones Using Twitter Corpus by Deep Learning Approach
Author : R. Pavithra and A. R. Mohamed ShanavasVolume 8 No.2 Special Issue:March 2019 pp 39-45
Abstract
Micro blogging websites are nothing but social media website to which user makes quick and frequent posts. Twitter is one of the well-known micro blog sites which offer the space for person which can read and put up messages that are 148 characters in duration. Twitter messages also are referred to as Tweets. And will use these tweets as raw facts. Then use a way that automatically extracts tweets into advantageous, bad or neutral sentiments. By the usage of the sentiment evaluation the consumer can recognize the feedback about the product or services before make a purchase. The organization can use sentiment evaluation to know the opinion of clients about their products, so can examine customer pleasure and in line with that they could improve their product. Now-a-days social networking sites are at the growth, so massive amount of data is generated. Millions of human beings are sharing their views each day on micro blogging sites, since it includes short and simple expressions. In this thesis, able to discuss approximately a paradigm to extract the sentiment from a famous micro running a blog carrier, Twitter, wherein customers submit their opinions for the whole thing. And can use the deep mastering algorithm to categories the twitters which incorporates Convolutional Neural Networks. The experimental end result is presented to demonstrate the use and effectiveness of the proposed system.
Keywords
Social Networking, Micro Blogging, Deep Learning, Sentiment Analysis, Convolutional Neural Network
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References
[1] Joshi, Rohit, and Rajkumar Tekchandani, “Comparative analysis of Twitter data using supervised classifiers”, Inventive Computation Technologies (ICICT), International Conference, IEEE,Vol. 3, 2016.
[2] Indriani, Fatma, and Dodon T. Nugrahadi, “Comparison of Naive Bayes smoothing methods for Twitter sentiment analysis”, Advanced Computer Science and Information Systems (ICACSIS), 2016 International Conference IEEE, 2016.
[3] Hussein, and Doaa Mohey El-Din Mohamed, “A survey on sentiment analysis challenges”, Journal of King Saud University-Engineering Sciences, 2016.
[4] Neri, Federico, et al., “Sentiment analysis on social media”, Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference, IEEE, 2012.
[5] G. Vinodhini, and R. M. Chandrasekaran, “Sentiment analysis and opinion mining: a survey”, International Journal, Vol. 2, No.6, pp. 282-292, 2012.
[6] K. Mouthami, K. Nirmala Devi, and V. Murali Bhaskaran, “Sentiment analysis and classification based on textual reviews”, Information communication and embedded systems (ICICES), 2013 international conference, IEEE, 2013.
[7] Cho, Sang-Hyun, and Hang-Bong Kang, “Text sentiment classification for SNS-based marketing using domain sentiment dictionary”, Consumer Electronics (ICCE), 2012 IEEE International Conference, IEEE, 2012.
[8] Khan, Aurangzeb, and Baharum Baharudin, “Sentiment classification using sentence-level semantic orientation of opinion terms from blogs”, National Postgraduate Conference (NPC), 2011.IEEE, 2011.
[9] Pan, SinnoJialin, et al., “Cross-domain sentiment classification via spectral feature alignment”, Proceedings of the 19th international conference on World wideweb. ACM, 2010
[10] Wu, Fangzhao, and Yongfeng Huang, “Sentiment domain adaptation with multiple sources”, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vol. 1, 2016.
[11] B. Pang, L. Lillian, and V. Shivakumar, “Thumbs up? Sentiment classification using machine learning techniques”, in Proc. ACL-02 Conf. Empirical Methods Natural Lang. Process., Vol. 10, pp. 79-86, Jul. 2002.
[12] M. Boia, B. Faltings, C.C. Musat, and P. Pu, “A 🙂 Is worth a thousand words: How people attach sentiment to emoticons and words in tweets”, in Proc. Int. Conf. Social Comput, pp. 345-350, Sep. 2013.
[13] K. Manuel, K. V. Indukuri, and P. R. Krishna, “Analyzing Internet slang for sentiment mining”,In Proc. 2nd Vaagdevi Int. Conf. Inform. Technol. Real World Problems, pp. 9-11, Dec. 2010.
[14] W. Gao and F. Sebastiani, “Tweet sentiment: From classification to quantification”,in Proc. IEEE/ACM Int. Conf. Adv. Social Netw. Anal. Mining (ASONAM), pp. 97-104, Aug. 2015.
[15] Y. H. P. P. Priyadarshana, K. I. H. Gunathunga, K. K. A. N. N. Perera, L. Ranathunga, P. M. Karunaratne, and T. M. Thanthriwatta, “Sentiment analysis: Measuring sentiment strength of call centre conversations”, in Proc. IEEE ICECCT, pp. 1-9, Mar. 2015.