Authors:Zeeshan Rasheed,Naeem Ahmed Ibupoto,Syeda Surriya Bano,Sheeraz Ahmed,
Keywords:social platform,social media,#tags,SQL,SA,API,
AbstractTwitter has now become the most common social platform to express views on any topic. A micro-blogging social media offers a way for people around the world to show their sentiments about any political, social and cultural subject of the time. In this paper, the sentimental analysis approach has been used to analyze the positive and negative sentiments of Twitter users about some top trending #tags around the globe. The data has been collected between the duration of March to April 2021. The collected data were processed by using the Python program and then transformed our data set with the help of the SQL database. We have used graphs and tables to present the data, collected under three hashtags; which were top trending topics on that particular era. The tweets were elaborated by positive, negative and neutral sentiments which were depicted in graphs. It is clear from the results and comparison that social media has a strong influence in the present era and can be highly helpful to use as a predictor of any political, social situation prevailing in any country or worldwide. It has also been helpful for business communities to analyze their products in the same manner to improve their business growth.
I. A. Giachanou and F. Crestani, ‘Like It or Not: A Survey of Twitter Sentiment Analysis Methods,’ ACM Comput. Surv., vol. 49, no. 2, pp. 1-41, 2016
II. Diakopoulos, N. & Shamma, D., 2010. Characterizing Debate Performance via Aggregated Twitter Sentiment. Proceedings of the 28th int. conference on Human factors in computing systems. Go, A., Bhayani, R. & Huang, L., 2009. Twitter Sentiment Classification using Distant Supervision.”
IV. Koyel Chakraborty, Sudeshna Sani, Rajib Bag,: ‘A STUDY ON SENTIMENT POLARITY IDENTIFICATION OF INDIAN MULTILINGUAL TWEETS THROUGH DIFFERENT NEURAL NETWORK MODELS’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 108-117. DOI : 10.26782/jmcms.2020.01.00008.
V. Larsen, Peder Olesen, and Markus von Ins. ‘The Rate of Growth in Scientific Publication and the Decline in Coverage Provided by Science Citation Index.’, Scientometrics 84.3 (2010): 575–603. PMC. Web. 25 Sept. 2015.”
VI. Liu, Bing, and Lei Zhang. ‘A survey of opinion mining and Sentiment Analysis (SA). Mining text data. Springer, Boston, MA, 2012. 415-463.”
VII. Omar bin Md Din, Abdul Ghani Bin Md Din, Rusdee Taher, Abduloh Usof, Prasert Panprae, Yousef A. Baker El-Ebiary. : ‘WEB CONTEXT AND THE MULTIPLE SEMANTIC LINGUISTIC ORIGINS AND ITS IMPACTS ON THE PROPHET’S TEXT. J. Mech. Cont.& Math. Sci., Vol.-15, No.-7, July (2020) pp 392-404. DOI : 10.26782/jmcms.2020.07.00033.
VIII. Polk, Alexander, and Patrick Paroubek. ‘Twitter as a corpus for Sentiment Analysis (SA) and opinion mining.’ LREc. Vol. 10. No. 2010. 2010.”
IX. Selmer, Øyvind, et al. ‘NTNU: Domain semi-independent short message sentiment classification.’ Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013). Vol. 2. 2013.”
X. Turney, Peter D. ‘Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews.’ Proceedings of the 40th annual meeting in association for computational linguistics. Association for Computational Linguistics, 2002.”
XI. Twitter – Wikipedia.