A survey on sentiment analysis and its applications

Analyzing and understanding the sentiments of social media documents on Twitter, Facebook, and Instagram has become a very important task at present. Analyzing the sentiment of these documents gives meaningful knowledge about the user opinions, which will help understand the overall view on these platforms. The problem of sentiment analysis (SA) can be regarded as a classification problem in which the text is classified as positive, negative, or neutral. This paper aims to give an intensive, but not exhaustive, review of the main concepts of SA and the state-of-the-art techniques; other aims are to make a comparative study of their performances, the main applications of SA as well as the limitations and the future directions for SA. Based on our analysis, researchers have utilized three main approaches for SA, namely lexicon/rules, machine learning (ML), and deep learning (DL). The performance of lexicon/rules-based models typically falls within the range of 55-85%. ML models, on the other hand, generally exhibit performance ranging from 55% to 90%, while DL models tend to achieve higher performance, ranging from 70% to 95%. These ranges are estimated and may be higher or lower depending on various factors, including the quality of the datasets, the chosen model architecture, the preprocessing techniques employed, as well as the quality and coverage of the lexicon utilized. Moreover, to further enhance models' performance, researchers have delved into the implementation of hybrid models and optimization techniques which have demonstrated an ability to enhance the overall performance of SA models.

Automated summary

Analyzing and understanding sentiments in social media documents is crucial for gaining insights into user opinions and overall perspectives on platforms like Twitter, Facebook, and Instagram. This paper provides an intensive review of sentiment analysis concepts and techniques, compares their performances, explores their applications, and discusses limitations and future directions. Researchers have utilized lexicon/rules, machine learning, and deep learning approaches for sentiment analysis. Performance ranges from 55-85% for lexicon/rules-based models, 55-90% for machine learning models, and 70-95% for deep learning models, reflecting variations based on factors such as dataset quality, model architecture, preprocessing techniques, and lexicon quality and coverage. Hybrid models and optimization techniques have been explored to further enhance performance.