Building a Custom Sentiment Analysis Tool based on an Ontology for Twitter Posts

Twitter is a popular micro-blogging platform which allows the users to share their opinion on any domain. The thoughts of the people vary according to the domain and also the opinion may contain both positive and negative words which are called as opinion words and are given in the form of dictionary called lexicon dictionary. The sentiment analysis done without feature extraction fails to give the deep result about the users opinion but in our proposed approach , features of the domain are extracted by building ontology which helps in getting the refined sentiment analysis. Feature based sentiment analysis gives the best result. While analyzing the sentiment, scores are assigned to the tweets so that the sentiment score of our tweets are compared with the third party like American Customer Satisfaction Index score. This comparison shows that our score assignment gives the detailed analysis of the features than the third party.