A Review On Opinion Mining Approaches, Practices And Application


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. Keywords: Sentiment Analysis, opinion mining, Machine Learning, Naïve Bayes, support vector machine.



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL