Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques

  • Mohammad W. Habib Computer Science Department, College of Science, Al-Nahrain University, Baghdad-Iraq
  • Zainab N. Sultani Computer Science Department, College of Science, Al-Nahrain University, Baghdad-Iraq
Keywords: Sentiment analysis, Natural language processing, Machine learning, Twitter data

Abstract

Twitter is considered a significant source of exchanging information and opinion in today's business. Analysis of this data is critical and complex due to the size of the dataset. Sentiment Analysis is adopted to understand and analyze the sentiment of such data. In this paper, a Machine learning approach is employed for analyzing the data into positive or negative sentiment (opinion). Different arrangements of preprocessing techniques are applied to clean the tweets, and various feature extraction methods are used to extract and reduce the dimension of the tweets' feature vector. Sentiment140 dataset is used, and it consists of sentiment labels and tweets, so supervised machine learning models are used, specifically Logistic Regression, Naive Bayes, and Support Vector Machine. According to the experimental results, Logistic Regression was the best amongst other models with all feature extraction techniques.

Published
2021-09-30
How to Cite
W. Habib, M., & N. Sultani, Z. (2021). Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques. Al-Nahrain Journal of Science, 24(3), 50-54. Retrieved from https://anjs.edu.iq/index.php/anjs/article/view/2372
Section
Articles