Sentiment Analysis of News Event-based Social Network using Data Mining Technique
Keywords:
sentiment analysis, support vector machine, naïve Bayesian, news-based social networking posts.Abstract
The increasing popularity of social media takes the attention of the internet users across the word wide to discuss and share the events/things they are interested on social media blogs/sites. Consequently, an explosive increase of social media data spread on the web has been promoting the development of analysis of social media news depending on the news or events, the latest trend of the social big data. The sentiment analysis of news event becomes an important research area for many real-world applications, such as public opinion monitoring for government and news recommendation of news websites. In this paper, we perform sentiment analysis for news events based on posts, and comments of the users upon a news event. We use two data mining techniques namely naïve Bayesian and support vector machine to reveal what the polarity/meaning of the post is such as positive, negative or polarity. There are two main stages in performing this task called training and testing phases. The first phase uses the training datasets of the news event and the second phase use newly inputted data of the user to classify the polarity of the user news posts or comments. We then execute the experiments for each algorithm and then collect the experimental results and compare them with accuracy with known and unknown test data with different volumes of tweet transactions. According to the results, both of them can accurately reveal the opinions of the social network users.
References
. Dandan Jiang, Xiangfeng Luo, Junyu Xuan, Zheng Xu, Sentiment Computing for the News Event based on the social media big data, Special Section on intelligent sensing on mobile and social media analytics, IEEE Access, vol. 5, 2017, pp-2373-2382.
. Xiaohui Yu, Yang Liu, Jimmy Xingji Huag, Aijun An, Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain, IEEE transactions on knowledge and data engineering, vol 24, no 4, 2012, pp-720-734.
. Danushka Bollegal, David Weir, John Carroll, Cross-Domain Sentiment Classification using a sentiment sensitive thesaurus, IEEE transactions on knowledge and data engineering, Vol 25, No. 8, 2013, pp-1719-1731.
. Zheng Xu, Shunxiag Zhang, Kim-kwang Raymond Choo, Lin Mei, Xiao Wei, Xiangfeng Luo, Chuanping, Yunhuai Liu, Hierarchy-cutting model based association semantic for analyzing domain topic on the web, IEEE transactions on industrial informatics, vol. 13, no. 4, 2017, pp-1941-1950.
. Baoshan Sun and Lingyu Dong, Dynamic Model Adaptive to User internet drift based on cluster and nearest neighbors, IEEE Access , vol 5, 2017, pp-1682-1691.
. X. Wang, F. Wei, X. Liu, M. Zhou, and M. Zhang, “Topic sentiment analysis in twitter: A graph-based hashtag sentiment classification approach,” in Proc. 20th ACM Int. Conf. Inform. Knowl. Manag., pp. 1031–1040, 2011.
. L. Zhuang, F. Jing, and X. Zhu, “Movie review mining and summarization,” in Proc. ACM 15th Conf. Inform. Knowl. Manag., Arlington, Virginia, USA, Nov. 2006, pp. 43–50.
. G. Erkan and D. R. Radev, “LexRank: Graph-based lexical centrality as salience in text summarization,” J. Artif. Intell. Res., vol. 22, pp. 457–479. 2004.
. R. McDonald, “A study of global inference algorithms in multidocument summarization,” in Proc. 29th Eur. Conf. IR Res., 2007, pp. 557–564.
. Yanzhang Lv, Jung Liu, Hao Chen, Jianhong Mi, Mengyue Liu, Ainghua Zheng, Opinioned Post Detection in Sian Weibo, Special Section on trust management in pervasive social networking, IEEE Access, 2017, pp-7263-7271.
. S. Zhang, J. Liang, R. He, and Z. Sun, ``Code consistent hashing based on information-theoretic criterion,'' IEEE Trans. Big Data, vol. 1, no. 3, pp. 84_94, Sep. 2015.
. J. Xuan et al., ``Uncertainty analysis for the keyword system of web events,'' IEEE Trans. Syst., Man, Cybern., Syst., vol. 46, no. 6, pp. 829_842, 2016.
. W. McDougall, An Introduction to Social Psychology. North Chelmsford, MA, USA: Courier Corporation, 2003.
. B. Pang, L. Lee, and S. Vaithyanathan, ``Thumbs up?: Sentiment classification using machine learning techniques,'' in Proc. ACL Conf. Empirical Methods Natural Lang. Process., 2002, pp. 79_86.
. H. Cui, V. Mittal, and M. Datar, ``Comparative experiments on sentiment classification for online product reviews,'' in Proc. AAAI, vol. 6. 2006, pp. 1265_1270.
. S.-M. Kim and E. Hovy, ``Automatic identification of pro and con reasons in online reviews,'' in Proc. COLING/ACL Main Conf. Poster Sessions, 2006, pp. 483_490.
. Monireh Ebrahimi, Amir Hossein Yazdavar, and Amit Sheth, Challenges of Sentiment Analysis for Dynamic Events, IEEE intelligent system, 2017, pp-70-75.
. Shuhui Jiang, Xueming Qian, Tao Mei, Yun Fu, Personalized travel sequence recommendation Multi-Source big social media, IEEE transaction on big data, vol. 2, no. 1, 2016, pp-43-56.
. H. Liu, T. Mei, J. Luo, H. Li, and S. Li, “Finding perfect rendezvous on the go: Accurate mobile visual localization and its applications to routing,” in Proc. 20th ACM Int. Conf. Multimedia, 2012, pp. 9–18.
. J. Li, X. Qian, Y. Y. Tang, L. Yang, and T. Mei, “GPS estimation for places of interest from social users’ uploaded photos,” IEEE Trans. Multimedia, vol. 15, no. 8, pp. 2058–2071, Dec. 2013.
. S. Jiang, X. Qian, J. Shen, Y. Fu, and T. Mei, “Author topic model based collaborative filtering for personalized POI recommendation,” IEEE Trans. Multimedia, vol. 17, no. 6, pp. 907–918, Jun. 2015.
. J. Sang, T. Mei, T. J. Sun, S. Li, and C. Xu, “Probabilistic sequential POIs recommendation via check-in data,” in Proc. ACM SIGSPATIAL Int. Conf. Adv. Geographic Inform. Syst., 2012, pp. 402–405.
. Feng Xia, Haifeng Liu, Longbing Cao, Scientific article recommendation: exploiting common author relations and historical preferences, IEEE transactions on big data, vol. 2, no.2, 2016, pp-101-112.
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