A comprehensive survey on sentiment analysis: Framework, techniques, and applications

The study of sentiment analysis (SA), also recognized as opinion mining, is a rapidly emerging area of study in natural language processing (NLP). This area focuses on identifying and extracting emotions and opinions from textual data, categorizing them as either positive, neutral, or negative. Nowadays, most of the people express their opinions on social networking platforms, often using their native languages. The rapid growth of Internet-based applications has given rise to a vast amount of personalized information and broad array of user reviews available online. There are a substantial number of pertinent reviews about a particular domain, which remain difficult for humans to process. Therefore, analyzing user opinions is crucial to extract meaningful insights and understand sentiments effectively. This survey comprehensively examines the spectrum of applications for sentiment analysis within the context of current studies. We then critically review experimental outcomes and limitations observed in cutting-edge studies. Furthermore, we explore lexicon-based methods, machine learning (ML), and deep learning (DL) strategies, as well as emerging techniques like transfer learning, large language models, and multimodal approaches, discussing their strengths as well as their weaknesses.

In addition, we employed multiple ML and DL strategies, leveraging the two benchmark IMDb and Yelp datasets. Following this, we utilized a systematic framework that incorporated preprocessing techniques, feature extraction, and evaluation metrics to facilitate comprehensive understanding and ensure model generalization. Finally, this study bridges the gap between traditional methods and modern innovations, addressing various challenges in sentiment analysis and proposing a roadmap for future research to mitigate these issues. This article serves as a guiding resource for researchers aiming to build an effective sentiment analysis framework.