Open AccessA Review on Opinion Mining: Approaches, Practices and Application
Author(s) -
Amaechi Chinedum,
Okeke Ogochukwu C
Publication year - 2021
Publication title -
international journal on recent and innovation trends in computing and communication
Language(s) - English
Resource type - Journals
ISSN - 2321-8169
DOI - 10.17762/ijritcc.v9i3.5456
Subject(s) - sentiment analysis , computer science , lexicon , focus (optics) , data science , social media , task (project management) , feeling , microblogging , information retrieval , artificial intelligence , data mining , world wide web , psychology , social psychology , physics , management , optics , economics
Opinion Mining also known as 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 individual's real-time feelings through the data resources provided by persons. The essential task is to extract user's tweets and implement an analysis and survey. However, this extracted information can very helpful to make prediction about the user's opinion towards specific policies. The motive of this paper is to perform a survey on sentiment analysis algorithms that shows the utilizing of different ML and Lexicon investigation methodologies and their accuracy. Our paper also focuses on the three kinds of machine learning algorithms for Sentiment Analysis- Supervised, Unsupervised Algorithms.