Predicting sentiment-mention associations in product reviews

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dc.contributor.author Vaswani, Vishwas
dc.date.accessioned 2012-04-27T15:49:30Z
dc.date.available 2012-04-27T15:49:30Z
dc.date.issued 2012-04-27
dc.identifier.uri http://hdl.handle.net/2097/13714
dc.description.abstract With the rising trend in social networking, more people express their opinions on the web. As a consequence, there has been an increase in the number of blogs where people write reviews about the products they buy or services they experience. These reviews can be very helpful to other potential customers who want to know the pros and cons of a product, and also to manufacturers who want to get feedback from customers about their products. Sentiment analysis of online data (such as review blogs) is a rapidly growing field of research in Machine Learning, which can leverage online reviews and quickly extract the sentiment of a whole blog. The accuracy of a sentiment analyzer relies heavily on correctly identifying associations between a sentiment (opinion) word and the targeted mention (token or object) in blog sentences. In this work, we focus on the task of automatically identifying sentiment-mention associations, in other words, we identify the target mention that is associated with a sentiment word in a sentence. Support Vector Machines (SVM), a supervised machine learning algorithm, was used to learn classifiers for this task. Syntactic and semantic features extracted from sentences were used as input to the SVM algorithm. The dataset used in the work has reviews from car and camera domain. The work is divided into two phases. In the first phase, we learned domain specific classifiers for the car and camera domains, respectively. To further improve the predictions of the domain specific classifiers we investigated the use of transfer learning techniques in the second phase. More precisely, the goal was to use knowledge from a source domain to improve predictions for a target domain. We considered two transfer learning approaches: a feature level fusion approach and a classifier level fusion approach. Experimental results show that transfer learning can help to improve the predictions made using the domain specific classifier approach. While both the feature level and classifier level fusion approaches were shown to improve the prediction accuracy, the classifier level fusion approach gave better results. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Sentiment analysis en_US
dc.subject Machine learning en_US
dc.title Predicting sentiment-mention associations in product reviews en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Computing and Information Sciences en_US
dc.description.advisor Doina Caragea en_US
dc.subject.umi Computer Science (0984) en_US
dc.date.published 2012 en_US
dc.date.graduationmonth May en_US


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