Abstract: Sentiment Analysis (SA) has recently become the focus of many researchers because analysis of online text is useful and demanded in many different applications. Analysis of social sentiments is a trending topic in this era because users share their emotions in more suitable format with the help of micro blogging services like twitter. Twitter provides information about individuals real-time feelings through the data resources provided by persons. The essential task is to extract users tweets and implement an analysis and survey. However, this extracted information can very helpful to make prediction about the users opinion towards specific policies. The motive of this study is to perform a survey on sentiment analysis algorithms that shows the utilizing of different ML and Lexicon investigation methodologies and their accuracy. The study also focuses on the three kinds of machine learning algorithms for Sentiment analysis-supervised, unsupervised algorithms.
Okeke Ogochukwu and Amaechi Chinedum, 2021. A Review on Sentiment Analysis: Approaches, Practices and Applications. Asian Journal of Information Technology, 20: 92-98.