Abstract
Processing Sentiment Analysis (SA) in social networks has lead decision makers to value opinion leaders who can sway people’s impressions concerning certain business or commodity. Yet, a tremendous scarceness of considering perspectivism, while computing text polarity, has been spotted. Considering perspectivism in SA can help in the production of polarity scores that represent the perceptible sentiment within the content. This emphasizes the necessity for integrating social behavior (user influence factor) with SA (text polarity scores), providing a more pragmatic portrayal of how text-recipients comprehend the message. In this paper, a novel model is proposed to intensify SA process in Twitter. In the achievement of such, UCINET tool and Artificial Neural Networks (ANN) are used for social network analysis (SNA) and users ranking respectively. For sentiment classification, a hybrid approach is presented—lexicon-based technique (TextBlob) along with fuzzy classification technique—to handle language vagueness as well as for an inclusive analysis of tweets into seven classes; for the purpose of enhancing final results. The proposed model is practiced on data collected from Twitter. Results show a significant enhancement in the final polarity scores, associated with the analyzed tweets, representing more realistic sentiments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Benedetto, F., Tedeschi, A.: Big data sentiment analysis for brand monitoring in social media streams by cloud computing. In: Studies in Computational Intelligence (2016)
Riquelme, F., González-Cantergiani, P.: Measuring user influence on Twitter: a survey. Inf. Process. Manag. (2016)
Priyanka, C., Gupta, D.: Fine grained sentiment classification of customer reviews using computational intelligent technique. Int. J. Eng. Technol. (2015)
Darwish, S.M., Madbouly, M.M., Hassan, M.A.: From public polls to Tweets: developing an algorithm for classifying sentiment from Twitter based on computing with words. J. Comput. (2016)
Haque, A., Rahman, T.: Sentiment analysis by using fuzzy logic. Int. J. Comput. Sci. Eng. Inf. Technol. (2014)
Wang, B., Huang, Y., Wu, X., Li, X.: A fuzzy computing model for identifying polarity of Chinese sentiment words. Comput. Intell. Neurosci. (2015)
Jefferson, C., Liu, H., Cocea, M.: Fuzzy approach for sentiment analysis. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2017)
Anjaria, M., Guddeti, R.M.R.: A novel sentiment analysis of social networks using supervised learning. Soc. Netw. Anal. Min. (2014)
Neves-Silva, R., Gamito, M., Pina, P., Campos, A.R.: Modelling influence and reach in sentiment analysis. Procedia CIRP 47, 48–53 (2016)
Bingol, K., Eravci, B., Etemoglu, C.O., Ferhatosmanoglu, H., Gedik, B.: Topic-based influence computation in social networks under resource constraints. IEEE Trans. Serv. Comput., 1 (2016)
Jianqiang, Z., Xiaolin, G., Feng, T.: A new method of identifying influential users in the micro-blog networks. IEEE Access (2017)
Hu, R.J., Li, Q., Zhang, G.Y., Ma, W.C.: Centrality measures in directed fuzzy social networks. Fuzzy Inf. Eng. (2015)
Yang, H., Li, Z.-F., Wei, W.: Instance analysis of social network based ucinet tool. Inf. Technol. J. 13(8), 1532–1539 (2014)
Hasan, A., Moin, S., Karim, A., Shamshirband, S.: Machine learning-based sentiment analysis for Twitter accounts. Math. Comput. Appl. (2018)
Yoshida, S., Kitazono, J., Ozawa, S., Sugawara, T., Haga, T., Nakamura, S.: Sentiment analysis for various SNS media using Naive Bayes classifier and its application to flaming detection. In: 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD), pp. 1–6. IEEE (2014)
Aleskerov, F., Meshcheryakova, N., Shvydun, S., Yakuba, V.: Centrality measures in large and sparse networks. In: 2016 6th International Conference on Computers Communications and Control, ICCCC (2016)
Hanneman, R., Riddle, M.: Introduction to Social Network Methods. University of California (2005)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Madbouly, M.M., Essameldin, R., Darwish, S. (2020). A Modified Fuzzy Sentiment Analysis Approach Based on User Ranking Suitable for Online Social Networks. In: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-31129-2_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31128-5
Online ISBN: 978-3-030-31129-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)