Twitter is a social media platform, a place where people from all parts of the world can make their opinions heard. Twitter produces around 500 million of tweets daily which amounts to about 8TB of data. The data generated in twitter can be very useful if analyzed as we can extract important information via opinion mining. Opinions about any news or launch of a product or a certain kind of trend can be observed well in twitter data. The main aim of sentiment analysis (or opinion mining) is to discover emotion, opinion, subjectivity and attitude from a natural text. In twitter sentiment analysis, we categorize tweets into positive and negative sentiment. Clustering is a protean procedure in which identically resembled objects are grouped together and form a pack or cluster. We conducted a study and found out that the use of clustering can quickly and efficiently distinguish tweets on the basis of their sentiment scores and can find weekly and strongly positive or negative tweets when clustered with results of different dictionaries. This paper implements the approach of clustering with respect to sentiment analysis and presents a way to find relationships between the tweets on the basis of polarity and subjectivity.

Opinion Mining, sentiment analysis, clustering, Twitter

[1] Peng, Zhichao, Qinghua Hu, and Jianwu Dang. ”Multi-kernel SVM based depression recognition using social media data.” International Journal of Machine Learning and Cybernetics (2017): 1-15.
[2] Banitaan, Shadi, and Kevin Daimi. ”Using data mining to predict possible future depression cases.” International Journal of Public Health Science (IJPHS) 3.4 (2014): 231-240.
[3] Abhyankar, Anjali. ”Social networking sites.” SAMVAD 2 (2011): 18-21.
[4] Braithwaite, Scott R., et al. ”Validating machine learning algorithms for twitter data against established measures of suicidality.” JMIR mental health 3.2 (2016).
[5] Tripathy, Abinash, Abhishek Anand, and Santanu Kumar Rath” Document-level sentiment classification using hybrid machine learning approach.” Knowledge and Information Systems (2017):1-27.
[6] Yousefpour, Alireza, Roliana Ibrahim, and Haza Nuzly Abdel amed. “Ordinal-based and frequency-based integration of feature selection methods for sentiment analysis” Expert Systems with Applications 75 (2017): 80-93.
[7] Hussain, Jamil, Maqbool Ali, Hafiz Syed Muhammad Bilal, Muhammad Afzal, Hafiz Farooq Ahmad, Oresti Banos, and Sungyoung Lee. ”SNS based predictive model for depression.” In International Conference on Smart Homes and Health Telematics, pp. 349-354. Springer, Cham,2015.
[8]R. Joshi and R. Tekchandani, "Comparative analysis of Twitter data using supervised classifiers," 2016 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, 2016, pp. 1-6. doi:10.1109/INVENTIVE.2016.7830089
[9] I.S. Jacobs and C.P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G.T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271-350.
[10] M. Kumar and A. Bala, "Analyzing Twitter sentiments through big data," 2016 3rd International Conference on Computing for Sustainable Global evelopment (INDIACom), New Delhi, 2016, pp. 2628-2631.
[11] R. A. Ramadhani, F. Indriani and D. T. Nugrahadi, "Comparison of Naïve Bayes smoothing methods for Twitter sentiment analysis," 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Malang, 2016, pp. 287-292.
[12] Deng, Li and Yu, Dong.Deep Learning: Methods and Applications.2014. NOW Publishers,United State of America.
[13] Miachel, Ray. 2012. 3 steps of text mining [Online] Available at: http://www2.cs.man.ac.uk/~raym8/comp38212/main/node203.html [Accessed 20 May 2017]
[14] Tomar, Shubham Simar.2017.Text Mining in R: A Tutorial [Online] Available at : https://www.springboard.com/blog/text-mining-in-r/[Accessed 20 May 2017]