Survey on Challenges and Solutions in Sentiment Analysis of Social Media Content

This review paper examines the problems and solutions that come with the sentiment analysis of social media content, finding challenges of informal language, slang, abbreviations, emojis, hashtags, cultural diversity, and contextual variations. Even though valuable insights into public opinion across various sectors can be obtained through sentiment analysis, dynamic and unstructured social media data create considerable problems. Advanced techniques will be looked at, from pre-processing techniques and domain-specific sentiment lexicons to deep learning models, such as BERT, all towards the purpose of increasing accuracy for sentiment classification, as well as integrating visual data, including emojis and hashtags, into the models. With the synthesis of recent work, the paper may succeed in capturing an understanding of the current state of the constantly changing landscape of social media sentiment analysis, illuminating efforts within striving for sentiment classification accuracy enhancement in online systems