Download PDFOpen PDF in browser

Comparison of Machine Learning Techniques on Twitter Emotions Classification

EasyChair Preprint no. 5518

12 pagesDate: May 13, 2021


Social media has become an essential part of our social life nowadays. A huge amount of user reviews and comments shared by users on social media. Twitter has an excellent growth in social media and also known as a platform for business and news. Text classification is an important part of text mining in recent years. Emotion mining is the science of detecting, analyzing, and evaluating humans’ feelings towards different events, issues, services, or any other interest. This paper discusses the Twitter text classification using various machine learning algorithms based on the emotions such as love, anger, anticipation, disgust, fear, joy, optimism, pessimism, sadness, surprise, trust, and neural. The performance of the classifiers Random Forest, Logistic Regression, and Stochastic Gradient Boost are analyzed and the results are compared.

Keyphrases: emotions classification, logistic regression, machine learning, Natural Language Processing, Random Forest, Stochastic Gradient Descent Boost, text classification, Twitter, twitter emotion classification

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {S Santhosh Baboo and M Amirthapriya},
  title = {Comparison of Machine Learning Techniques on Twitter Emotions Classification},
  howpublished = {EasyChair Preprint no. 5518},

  year = {EasyChair, 2021}}
Download PDFOpen PDF in browser