A new big data framework for customer opinions polarity extraction

Abstract

Recently, we are talking about opinion mining: It refers to extract subjective information from text data using the natural language processing, text analysis and computational linguistics. Micro-blogging is one of the most popular Web 2.0 applications, such as Twitter which is evolved into a practical means for sharing opinions around different topics. It becomes a rich data sources for opinion mining and sentiment analysis. In this work, we interest by to study users opinions about an object in social networks, for example studying the opinion of users about “the Samsung brand” or “the nokia brand”, using text mining and NLP (Natural language processing) technologies. We propose a new ontological approach able to determinate the polarity of user post. This approach classify the users posts to negative, positive or neutral opinions. To validate the effectiveness of our approach, we used a dataset published by Bing Liu’s group in our approach experimentation.