Pandey, Suraj Jung (2011) Opinion Analysis through Constraint Optimisation. MSc by research thesis, University of York.
Available under License Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales.
Opinion lexicon plays a vital role in sentiment classification. A previous study shows that a compositional model can be eective in sentiment classification. But such a model has been only applied using hand-crafted composition rules. The need for hand-crafted rules arise when dealing with conflicting polarity values within the same phrase. In this thesis, we show that an alternative is to employ a weighted polarity lexicon. There are several key advantages of a weighted polarity lexicon. Firstly, compositionality rules simply become linear sums without requiring conflict resolution rules. Secondly, a weighted polarity lexicon can be automatically learnt from review data using constraint optimisation. Thirdly, instead of providing just a binary positive or negative output, our model can be used to provide a graded overall sentiment. Our experiments show that our model provides state-of-the-art opinion classication.
|Item Type:||Thesis (MSc by research)|
|Department:||The University of York > Computer Science (York)|
|Deposited By:||Suraj Jung Pandey|
|Deposited On:||26 Aug 2011 11:15|
|Last Modified:||26 Aug 2011 11:15|
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