Twitter Sentimental Analysis on Fan Engagement

  • Rasika Shreedhar Bhangle
  • K. Sornalakshmi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 645)


Sentimental analysis involves determination of opinions, feelings, and subjectivity of text. Twitter is a social networking service where millions of people share their thoughts. Twitter sentimental analysis on fan engagement focuses on how fans in different sports industry actively engage on social media. Fans are identified based on their opinions and emotions expressed in the tweet. In this paper, we provide a comparative analysis of machine learning algorithms such as multinomialNB, support vector machine, linearSVM, and decision trees. The traditional approach involves manually assigning labels to training data which is time-consuming. To overcome this problem, we use TextBlob which computes the sentiment polarity based on POS tagging and assigns sentiment score to every tweet in range of −1 to +1. We compute the subjectivity of text which is in the range of 1–0. We also identify the sarcastic tweets and assign correct class labels based on the weightage we provide for every word. Our experimental result shows how accuracy gets increased with the use of advanced machine learning algorithms.


Twitter Sentimental analysis Machine learning Analytics Naïve bayes MultinomialNB Support vector machine LinearSVM Decision trees Polarity analysis MongoDB Python 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information Technology (Big Data Analytics)SRM UniversityChennaiIndia
  2. 2.Department of Information TechnologySRM UniversityChennaiIndia

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