Big data analysis to Features Opinions Extraction of customer

Abstract Opinion mining refers to extract subjective information from text data using tools such as natural language processing (NLP), text analysis and computational linguistics. Micro-blogging and social network are the most popular Web 2.0 applications, like Twitter and Facebook which are developed for sharing opinions about different topics. This kind of application becomes a rich data source for opinion mining and sentiment analysis. This information is crucial for managers, who should improve the quality of a product based on customers’ opinions. Concerning the characteristic of a product as mobile phone, it is particularly difficult to identify the features being commented on (e.g., camera quality, battery life, price, etc). In our work, we present a new method that able to extract product features opinions of customer from social networks using text analysis techniques. This task identifies customers opinions regarding product features. We develop a system for retrieving tweets about a product from Twitter and detect product features opinions and their polarity. To validate the effectiveness of this approach, we used a dataset published by Bing Lius group in our approach experimentation. This dataset contains many notated customer reviews of five products such as Canon G3 and Nokia 6610. Next, we test this method with tweets retrieved from Twitter about Nokia, Samsung and Iphone features products.