Classification of Tweets Using Dictionary and Lexicon Based Approaches
Online social media is pervasive in nature. It allows people to use short text messages, images, audios and videos to express their opinions and sentiments about products, events and other people. For example, Twitter is an online social networking and news service where users post and interact with small and short messages, called “tweets”. Therefore, nowadays social media become a potential source for business and celebrities to find people’s sentiments and opinions about a particular event or product or themselves.
Social media analysis is the process of gathering enormous amount of digital contents generated online from blogs sites and social media networks and examining them to find the insights.
This paper focuses on discovering public opinions and sentiments he on the results of Indian election results declared recently. This Paper also deals Dictionary based Approach and Affective Lexicon based approaches which were used to find the public opinion about election results.
KeywordsSocial media data Opinion Mining (OM) Sentiment Analysis (SA) Sentiment Analyzer
- 1.Dunđer, I., Horvat, M., Lugović, S.: Word occurrences and emotions in social media: case study on a Twitter corpus, pp. 1284–1287. IEEE (2016)Google Scholar
- 2.Mishra, P., Rajnish, R., Kumar, P.: Sentiment analysis of Twitter data: case study on digital India. In: 2016 International Conference on Information Technology (InCITe) - The Next Generation IT Summit on the Theme - Internet of Things: Connect your Worlds, Noida, pp. 148–153 (2016)Google Scholar
- 3.https://twitter.com/login?lang=en. Accessed June 2019
- 4.Jayamalini, K., Ponnavaikko, M.: Social media mining: analysis of Twitter data to find user opinions about GST. J. Eng. Appl. Sci. 14(12), 4167–4175 (2019)Google Scholar
- 5.Jayamalini, K., Ponnavaikko, M.: Enhanced social media metrics analyzer using twitter corpus as an example. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 8(7), 822–828 (2019)Google Scholar
- 7.Akter, S., Aziz, M.T.: Sentiment analysis on Facebook group using lexicon based approach. In: 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, pp. 1–4 (2016)Google Scholar
- 8.Karamollaoğlu, H., Doğru, İ.A., Dörterler, M., Utku, A., Yıldız, O.: Sentiment analysis on Turkish social media shares through lexicon based approach. In: 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, pp. 45–49 (2018)Google Scholar
- 9.Tiara, Sabariah, M.K., Effendy, V.: Sentiment analysis on Twitter using the combination of lexicon-based and support vector machine for assessing the performance of a television program. In: 2015 3rd International Conference on Information and Communication Technology (ICoICT), Nusa Dua, pp. 386–390 (2015)Google Scholar
- 10.Cheng, D., Schretlen, P., Kronenfeld, N., Bozowsky, N., Wright, W.: Tile based visual analytics for Twitter big data exploratory analysis. In: 2013 IEEE International Conference on Big Data, Silicon Valley, CA, pp. 2–4 (2013)Google Scholar
- 11.Jayamalini, K., Ponnavaikko, M.: Research on web data mining concepts techniques and applications. In: 2017 International Conference on Algorithms Methodology Models and Applications in Emerging Technologies (ICAMMAET), Chennai, pp. 1–5 (2017)Google Scholar
- 12.https://en.wikipedia.org/wiki/Social_media. Accessed June 2019