In recent years people want to express their opinion on every online service or product, and there are now a huge number of opinions on the social media, online stores and blogs. However, most of the opinions are presented in plain text and thus require a powerful method to analyse this volume of unlabeled reviews to obtain information about relevant details in minimum time and with a high accuracy. In this thesis we propose a supervised model to analyze large unlabeled opinion data sets. This model has two phases: preprocessing and a Supervised Sentiment and Aspect Model (SSAM). In the preprocessing phase we input thousands of unlabeled opinions and received a set of (key, value) pairs in which a key holds a word or an opinion and a value holds supervised information such as a sentiment label of this word or opinion. After that we give these pairs to the proposed SSAM algorithm, which incorporates different levels of supervised information such as document and sentence levels or document and term levels of supervised information, to extract and cluster aspects related to a sentiment label and also left; MARGIN: 0in 0in 0pt; unicode-bidi: embed; DIRECTION: ltr" dir=ltr align=left Keywords: Big unlabeled opinion dataset, Supervised Sentiment and Aspect Model, Supervised and Unsupervised methods, Supervised information
