Combining Granularity-based Topic-Dependent and Topic-Independent Evidences for Opinion Detection

Opinion mining is a subdiscipline within Information Retrieval (IR) and Computational Linguistics. It refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online sources like news articles, social media comments, and other user-generated content. It is also known by many other terms like opinion nding, opinion detection, sentiment analysis, sentiment classi cation, polarity detection, etc. De ning in more speci c and simpler context, opinion mining is the task of retrieving opinions on an issue as expressed by the user in the form of a query. There are many problems and challenges associated with the eld of opinion mining. In this thesis, we focus on some major problems of opinion mining. One of the foremost and major challenges of opinion mining is to nd opinions specifically relevant to the given topic (query). A document can contain information about many topics at a time and it is possible that it contains opinionated text about each of the topic being discussed or about only few of them. Therefore, it becomes very important to choose topic-relevant document segments with their corresponding opinions. We approach this problem on two granularity levels, sentences and passages. In our rst approach for sentence-level, we use semantic relations of WordNet to nd this opinion-topic association. In our second approach for passage-level, we use more robust IR model (i.e., language model) to focus on this problem. Basic idea behind both contributions for opinion-topic association is that if a document contains more opinionated topic-relevant textual segments (i.e., sentences or passages) then it is more opinionated than a document with less opinionated topic-relevant textual segments. Most of the machine-learning based approaches for opinion mining are domain-dependent (i.e., their performance vary from domain to domain). On the other hand, a domain or topic-independent approach is more generalized and can sustain its e ectiveness across di erent domains. However, topic-independent approaches su er from poor performance generally. It is a big challenge in the eld of opinion mining to develop an approach which is both e ective and generalized at the same time. Our contribu-