Search

People’s opinions are considered as the most powerful source of market research. Popularly, Social Media has become a tool that is used in an easy way including huge number of users who can share their opinions about products or services and their thoughts about current problems of the society and express their views on political and religious issues. The knowledge extracted from social media contains sentiment data – that is not included in corporate database – that can be used to improve the marketing campaigns to retain customers and meet their needs in a better way. The integration and merging between both social media data and corporate data can lead to better insights that would not have been possible to gain without such integration. In this paper, we will use Twitter as a social media source platform to do a feature based level sentiment analysis using tweets including opinions about a specific product. . The research discussed three different ways to extract (feature/opinion) pairs from each text including: Normal Tokenization, N-gram Modeling Extraction, and Noun Chunking Extraction. The extracted opinion phrase related to each extracted feature is being classified using sentiment classification algorithm. A decision is taken about the best between the three ways according to the resulted measurements. SCDJF had been evaluated using multiple techniques. The best results occurred from Noun Chunking Extraction with accuracy 77%. Summarization of the results will show how this can be used to enhance decision making process of the organization. Summarization of the results will show how this can be used to enhance decision making process of the organization.