Machine Learning-Based Sentiment Analysis for Twitter Accounts

• Main Authors: Ali Hasan, Sana Moin, Ahmad Karim, Shahaboddin Shamshirband
• Format: Article
• Language: English
• Published: MDPI AG 2018-02-01
• Series: Mathematical and Computational Applications
• Subjects: Twitter; sentiment analyzer; machine learning; WordNet; word sequence disambiguation (WSD); Naïve Bayes
• Online Access: http://www.mdpi.com/2297-8747/23/1/11

Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various machine-learning techniques and tools for sentiment analysis during elections, there is a dire need for a state-of-the-art approach. To deal with these challenges, the contribution of this paper includes the adoption of a hybrid approach that involves a sentiment analyzer that includes machine learning. Moreover, this paper also provides a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naïve Bayes and support vector machines (SVM).