Sentiment Analysis of Twitter Classification by Applying Hybrid-Based Techniques

Abstract

AbstractAs technology advances, the world is rapidly transmuting as a result of current technologies, and specially, the Internet has grown ingrained in everyone’s lives. The various social networking applications, namely Twitter, Facebook and Google+ are rapidly gaining huge popularity and most pertinently, Twitter has emerged to be the utmost popular platform for the individuals to coin their opinions and share their experiences with regard to various issues related to products and services. This harvesting of opinions by analysing the vast volume of unstructured information which are a result of social media sites is really a very tedious job. Sentiment analysis or opinion mining is a technique that aims to unveil the sentiments of the common people by analysing textual dissection and helps opinion formation regarding diversified areas. Sentiment analysis can assist researchers to gain insight with respect to numerous public issues specifically in marketing, business, products, services, politics, companies, governments etc. Sentiment analysis is a methodology of transforming unstructured data into structured data by classifying text attributes such as sentiment orientation and differentiate them into positive and negative sentiment category and has evolved in popularity as a subject of study in the modern era. The application of machine learning algorithms contribute significantly in the classification of sentiment analysis. This paper proposes both lexicon-based and machine learning techniques and aims to discuss about the current state-of-the-art of sentiment analysis techniques employed in the classification of tweet sentiment orientation and also addresses the challenges that can be employed to enhance the process of analysis, summarizing and classification of opinion mining.KeywordsSentiment analysis (SA)Opinion mining (OM)Natural language processing (NLP)Machine learningNLTK and lexicon