Challenges and Issues in Sentiment Analysis: A Comprehensive Survey

Abstract

Sentiment analysis, a specialization of natural language processing (NLP), has witnessed significant progress since its emergence in the late 1990s, owing to the swift advances in deep learning techniques and the abundance of vast digital datasets. Though sentiment analysis has reached a relatively advanced stage in the area of NLP, it is erroneously assumed that sentiment analysis has reached its pinnacle, leaving no room for further improvement. However, it is important to acknowledge that numerous challenges that require attention persist. This survey paper provides a comprehensive overview of sentiment analysis, including its applications, approaches to sentiment classification, and commonly used evaluation metrics. The survey primarily focuses on the challenges associated with different types of data for sentiment classification, namely cross-domain data, multimodal data, cross-lingual data, and small-scale data, and provides a review of the state-of-the-art in sentiment analysis to address these challenges. The paper also addresses the challenges faced during sentiment classification irrespective of the type of data available. It aims at a better understanding of sentiment analysis to enable practitioners and researchers select suitable methods for sentiment classification depending on the type of data being analyzed.

Keywords

• Machine learning
• sentiment analysis
• natural language processing
• cross-domain data
• multimodal data
• cross-lingual data