Machine Learning-Based Sentiment Analysis for Twitter Accounts

By Ali Muttaleb Hasan, Sana Moin, Ahmad Karim, Shahaboddin Shamshirband. 2018

403 citations recorded by the source corpus.

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 Naive Bayes and support vector machines (SVM).

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/959c9dbfceda3825787f75f63fd7c87f332dc271

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In Artificial Intelligence

1. Ringki Das
2. Thoudam Doren Singh

Citations in the corpus

• Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, methodology): “Hasan et al. [41] adopted the machine learning approach with unigram features for binary sentiment analysis on the Twitter dataset.”