Aspect Miner: Fine-grained, feature-level opinion mining from rated review corpora
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MSc Thesis defense ...
MSc Thesis defense
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Abstract: The web offers vast quantities of user-generated content, including reviews. These reviews, be they about products, services, books, music or movies, constitute a primary target for the application of opinion analysis techniques. We present Aspect Miner, an integrated opinion mining system tailored to user reviews published on the web. By leveraging the user ratings that typically accompany these reviews, Aspect Miner can be trained to distinguish not only positive from negative sentiment, but also between multiple sentiment intensity levels. Moreover, Aspect Miner is able to classify opinions on the sentence level as well as on the level of individual ratable aspects that are present in a sentence, and is adaptable to texts of any domain.
The system is built around three core subtasks: (i) classification of subjective terms (ii) aspect identification and (iii) sentence sentiment analysis. For the first subtask, we pro-pose a classification scheme that employs the user ratings in a training corpus. For the second one, we look into the LDA topic model as a means to identify and extract the features of the reviews items in the corpus and we attempt to address its inherent limitations by employing an additional post-processing step that aggregates multiple disparate feature models into a single concise one. Finally, in order to perform analysis on the sentence level, we make use of the results of the aforementioned subtasks together with a syntax-tree based linguistic method powered by a set of predefined typed dependency rules. Our experiments show that the accuracy of our approach on these specific tasks is at least comparable to – and under certain circumstances surpasses – a number of other popular sentiment analysis techniques.
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