Sentiment analysis of texts by capturing underlying sentiment patterns

With the rapid proliferation of online-blogging and micro-blogging websites, millions of text posts are generated and made available online every day. Utilizing this rich data channel could facilitate educated purchasing of items, discovering trends and public tendencies regarding various products available in the market, discovering political inclination of societies prior to a national election, etc. Since the last decade, Sentiment Analysis (SA) has received increased attention from many researchers as a method for addressing topics, such as the aforementioned ones. This paper focuses on SA using sentiment features and patterns. We propose different sentiment polarity detection methods, two unsupervised methods and one supervised, which we compare with two baseline methods, a state-of-the-art Support Vector Machine (SVM) classifier trained on a unigram bag-of-words model, and an unsupervised SentiStrength (38) algorithm. In our experiments, we show that our polarity detection methods are highly effective and can outperform the aforementioned baselines in most of our conducted experiments.