Abstract
The growing public endorsement of social media has changed public life dramatically. Public views and suggestions have now become important for both organizations and individuals. Big data scientists and data mining analysts are increasingly moving their attention toward sentiment analysis because of the growing rate of user-generated contents over microblogging sites. Sentiment analysis is a research field related to computationally identifying public views, feelings, recommendations, opinions and sentiments about focused entities. Research literature shows traces of research work on product and movie reviews for better decision making using big data analysis. Big data analytics offer remarkable opportunities to individuals as well as organizations by providing proficient decision making frameworks and improved forecasting models. The sociopolitical collaboration has gained much attention from online users over the past few years. In this research we analyzed public views, sentiments and opinions shared on social media about a democratic participatory activity called Azadi-March, which was held in Pakistan with participation of online users from all over the world. We carried out computational semantic orientation on public tweets for analyzing public awareness and the effects of online communication through social media over the real world public decision making. We employed unsupervised approach for identification and scoring of tweets. We used lexicon based approach in which annotated lexica are used for scoring verbs, adverbs and other parts of speech. A corpus is used for scoring adjectives and informal opinion indicators. Emoji, exclamatory statements and other additional features are incorporated for supplementary analysis. We noticed that emoticons and NetLingo play significant role in sentiment orientation. Opinion groups are generated from all retrieved tweets and aggregate sentiment weights of opinion groups are computed. The findings of this study indicate that our proposed lexicon based approach outperforms the contemporary machine learning techniques by achieving 86% average accuracy at sentence level sentiment analysis.
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Aday, S., Farrell, H., Lynch, M., Sides, J., Kelly, J. , Zuckerman, E.: Blogs and bullets: New media in contentious politics. United States Institute of Peace, (65) (2010)
Amiri, H., Chua, T.-S.: Mining NetLingua and urban opinion words and phrases from cQA services. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining - WSDM ’12, pp. 193 (2012)
Arif, M.H., Li, J., Iqbal, M., Liu, K.: Sentiment analysis and spam detection in short informal text using learning classifier systems. Journal of Soft Computing, 1–11 (2017)
Balahur, A.: Sentiment analysis in social media texts. In: 4th workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp 120–128 (2013)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)
Chowdhury, M.F.M., Guerini, M., Tonelli, S., Lavelli, A.: Fbk: Sentiment analysis in twitter with tweetsted. In: Second Joint Conference on Lexical and Computational Semantics (* SEM), vol. 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), vol. 2, pp 466–470 (2013)
Choy, M., Cheong, M., Laik, M.N., Shung, K.P.: Us presidential election 2012 prediction using census corrected twitter model. arXiv:1211.0938 (2012)
Chung, J.E., Mustafaraj, E.: Can collective sentiment expressed on twitter predict political elections?. In: AAAI, vol. 11, pp 1770–1771 (2011)
Denecke, K.: Are SentiWordNet scores suited for multi-domain sentiment classification?. In: 2009. ICDIM 2009. Fourth International Conference on Digital Information Management, pp. 1–6. IEEE (2009)
Devika, M.D., Sunitha, C., Ganesh, A.: Sentiment Analysis: A Comparative Study on Different Approaches. Procedia Computer Science 87, 44–49 (2016)
Diakopoulos, N.A., Shamma, D.A.: Characterizing debate performance via aggregated twitter sentiment. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1195–1198. ACM (2010)
Elyasir, A.M.H., Anbananthen, K.S.M.: Opinion mining framework in the education domain. International Journal of Social Human Science and Engineering 7(4) (2013)
Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of LREC, vol. 6, pp 417–422 (2006)
Gayo-Avello, D.: A warning against converting social media into the next literary digest. Communications of the ACM (2011)
Hasan, A., Moin, S., Karim, A., Shamshirband, S.: Machine Learning-Based Sentiment Analysis for Twitter Accounts. Mathematical and Computational Applications 23(1), 11 (2018)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, pp. 174–181. Association for Computational Linguistics (1997)
Ibrahim, H.S., Abdou, S.M., Gheith, M.: Sentiment analysis for modern standard arabic and colloquial (2015)
Khan, F.H., Qamar, U., Bashir, S.: Lexicon based semantic detection of sentiments using expected likelihood estimate smoothed odds ratio. Artif. Intell. Rev. 48(1), 113–138 (2017)
Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal text. J. Artif. Intell. Res. 50, 723–762 (2014)
Kumar, A., Sebastian, T.M.: Sentiment analysis on twitter. IJCSI International Journal of Computer Science Issues 9(4), 372 (2012)
Liu, B.: Sentiment analysis and opinion mining. Synthesis lectures on human language technologies 5(1), 1–167 (2012)
Livne, A., Simmons, M.P., Adar, E., Adamic, L.A.: The party is over here: structure and content in the 2010 election. ICWSM 11, 17–21 (2011)
Manke, S.N., Shivale, N.: A review on: opinion mining and sentiment analysis based on natural language processing. Int. J. Comput. Appl. 109(4), 29–32 (2015)
Meoni, M., Perego, R., Tonellotto, N.: Dataset Popularity Prediction for Caching of CMS Big Data. Journal of Grid Computing 16(2), 211–228 (2018)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Mishra, D., Venugopalan, M., Gupta, D.: Context Specific Lexicon for Hindi Reviews. Procedia Computer Science 93, 554–563 (2016)
Nasukawa, T., Yi, J.: Sentiment analysis: Capturing favorability using natural language processing. In: Proceedings of the 2nd International Conference on Knowledge Capture, pp. 70–77. ACM (2003)
O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: Linking text sentiment to public opinion time series. ICWSM 11(122-129), 1–2 (2010)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1-2), 1–135 (2008)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
Peckham, A.: urban dictionary, Available at:, http://www.urbandictionary.com/ (Accessed: 19 September 2015) (1999)
Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowl.-Based Syst. 89, 14–46 (2015)
Raza, A.A., Habib, A., Ashraf, J., Javed, M.: A review on Urdu language parsing. Int. J. Adv. Comput. Sci. Appl. 8(4), 93–97 (2017)
Sarker, A., Gonzalez, G.: DIEGOLab16 at SemEval-2016 Task 4: Sentiment analysis in Twitter using centroids, clusters, and sentiment lexicons. In: Proceedings of SemEval, pp 209–214 (2016)
Serrano-Guerrero, J., Olivas, J.A., Romero, F.P., Herrera-Viedma, E.: Sentiment analysis: a review and comparative analysis of Web services. Inf. Sci. 311, 18–38 (2015)
Skoric, M., Poor, N., Achananuparp, P., Lim, E.P., Jiang, J.: Tweets and votes: A study of the 2011 singapore general election. In: 2012 45th Hawaii International Conference on System Science (HICSS), pp. 2583–2591. IEEE (2012)
Sunstein, C.R.: The law of group polarization. J. Polit. Philos. 10(2), 175–195 (2002)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)
Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. ICWSM 10, 178–185 (2010)
Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)
Williams, L., Bannister, C., Arribas-Ayllon, M., Preece, A., Spasić, I.: The role of idioms in sentiment analysis. Expert Systems with Applications 42(21), 7375–7385 (2015)
Yang, M., Tu, W., Lu, Z., Yin, W., Chow, K.P.: LCCT: a semi supervised model for sentiment classification. In: Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL. Association for Computational Linguistics (ACL) (2015)
Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, pp. e1253 (2018)
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Raza, A.A., Habib, A., Ashraf, J. et al. Semantic Orientation Based Decision Making Framework for Big Data Analysis of Sporadic News Events. J Grid Computing 17, 367–383 (2019). https://doi.org/10.1007/s10723-018-9466-y
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DOI: https://doi.org/10.1007/s10723-018-9466-y