A Review of Sentiment Analysis in Social Media Perspectives

Abstract

The widespread use of the Internet and social media platforms has led to an increase in the number of individuals who declare their feelings publicly. Therefore, sentiment analysis systems have proceeded because of their crucial role in determining the personal opinions of users. This is can greatly influence the decision-making process in various fields. To create a robust and reliable sentiment analysis system, it was necessary to apply techniques capable of dealing with these scattered opinions. Natural language processing techniques are commonly used to extract information from unstructured text data published by humans. The comments and posts in social media platforms are often ignore the grammar rules and sentence structure. This is resulting in many ambiguities in lexical, syntactic, and semantic aspects. As a result, researchers have developed different methods for text mining and defining real information. This survey aims to study the different methods used in sentiment analysis filed. We discussed two common models of classification, including the vocabulary-based model and the supervision-based approach.