Opinion Mining Using Twitter Feeds for Political Analysis

Manogna Meduru, Antara Mahimkar, Krishna Subramanian, Puja Y. Padiya, Prathmesh N. Gunjgur

Abstract


Sentiment analysis deals with identifying and understanding opinions and sentiments expressed in a particular text. The masses give their opinion regarding various subjects on social media platforms using tweets, status updates and blogs. By analyzing this very data, we can gain better insight of the public opinion on any subject in specific. On performing sentiment analysis in a specific domain, it is possible to identify the effect of domain information in sentiment classification. Twitter sentiment analysis is difficult compared to general sentiment analysis due to the presence of slang words and misspellings. The maximum limit of characters allowed in Twitter is 140. In this paper, we try to analyze the twitter posts about government issues and political reforms. The proposed framework uses Twitter as the platform to analyze the emotions of the users using Sentiment Analysis. The system will use the opinions of the users, analyze the reaction and then map it to the appropriate region.


Keywords


Opinion mining/Sentiment Analysis; Natural language Toolkit(NLTK); Political Analysis.

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References


Delip Rao, Deepak Ravichandran. (2009). Semi-Supervised Polarity Lexicon Induction.

Delenn Chin, Anna Zappone, Jessica Zhao. Analyzing Twitter Sentiment of the 2016 Presidential Candidates.

Filipe N Ribeiro, Matheus Ara´ujo, Pollyanna Gonc¸alves, Marcos Andr´e Gonc¸alves, Fabr´ıcio Benevenuto. (2016, July). SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods.

C.J. Hutto, Eric Gilbert. (2014).VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text.

Bo Yuan. (2016). Sentiment Analysis of Twitter Data.

Andranik Tumasjan, Timm O. Sprenger, Philipp G. Sandner, Isabell M. Welpe. (2010). Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment.

Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani|, Veselin Stoyanov. (2016, June). SemEval-2016 Task 4: Sentiment Analysis in Twitter.

Pang, B., Lee, L. & Vaithyanathan, S. (2002). Sentiment Classification using Machine Learning Techniques.

Gebrekirstos Gebremeskel. (2011, May). Sentiment Analysis of Twitter Posts about News.

Effective Text Data Cleaning [Online]. Available

https://www.analyticsvidhya.com/blog/2015/06/quick-guide-text-data-cleaning-python/

Mining and Preprocessing Twitter Data with Python [Online]. Available

https://marcobonzanini.com/2015/03/09/mining-twitter-data-with-python-part-2/

Useful Pandas Techniques in Python for Data Manipulation [Online].Available https://www.analyticsvidhya.com/blog/2016/01/12-pandas-techniques-python-data-manipulation/


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