Open AccessTwitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques
Author(s) -
Mohammad W. Habib,
Zainab N. Sultani
Publication year - 2021
Publication title -
al-nahrain journal of science
Language(s) - English
Resource type - Journals
eISSN - 2663-5461
pISSN - 2663-5453
DOI - 10.22401/anjs.24.3.08
Subject(s) - sentiment analysis , naive bayes classifier , computer science , artificial intelligence , preprocessor , support vector machine , machine learning , feature extraction , data pre processing , feature (linguistics) , dimension (graph theory) , logistic regression , data mining , mathematics , philosophy , linguistics , pure mathematics
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.