Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)

Anam Yousaf, Muhammad Umer, Saima Sadiq, Saleem Ullah, Seyedali Mirjalili, Vaibhav Rupapara, Michele Nappi

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


The proliferation of user-generated content on social media has made opinion mining an arduous job. As a microblogging platform, Twitter is being used to collect views about products, trends, and politics. Sentiment analysis is a technique used to analyze the attitude, emotions and opinions of different people towards anything, and it can be carried out on tweets to analyze public opinion on news, policies, social movements, and personalities. By employing Machine Learning models, opinion mining can be performed without reading tweets manually. Their results could assist governments and businesses in rolling out policies, products, and events. Seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. With an in-depth comparative performance analysis, it was observed that proposed voting classifier(LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score. To further validate stability of the proposed approach on two more datasets, one binary and other multi-class dataset and achieved robust results.

Original languageEnglish
Article number9309291
Pages (from-to)6286-6295
Number of pages10
JournalIEEE Access
Publication statusPublished - 2021


  • artificial intelligence
  • emotion recognition
  • machine learning
  • opinion mining
  • Sentiment analysis
  • text classification


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