A Comprehensive Analysis of the Current Methods, Challenges and Innovations in Sentiment Analysis

Abstract

Sentiment analysis, also referred to as opinion mining, is an important segment of natural language processing (NLP) and machine learning that textual data targets based on the identification or classification of emotions, opinions, sentiments, or attitudes. Initial works associated with the study of public opinion and the subjectivity of text date from the beginning of the 20th century, while sentiment analysis started to take flight in the late 1990s with the upsurge of user-generated content on the internet. While early approaches were lexicon-based, thereby based on predefined word lists, lately, these have evolved to include machine learning models such as Naive Bayes, Support Vector Machines (SVMs), and deep learning techniques like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Of late, research has focused more on fine-grained or subtle sentiment analysis that faces challenges like sarcasm, context-based meaning, and generalization across domains. It has further evolved into multimodal analysis, combining text with other forms of media, such as images and audio. In spite of this, all challenges remain in cross-lingual sentiment detection and ethical considerations within sentiment analysis. Future research will probably be directed at enhancing the accuracy of the models, real-time processing, and addressing language diversity in the application of sentiment analysis.