Please use this identifier to cite or link to this item:
|Source:||Amiri, H.,Chua, T.-S. (2012). Sentime. AAAI Workshop - Technical Report WS-12-09 : 39-42. ScholarBank@NUS Repository.|
|Abstract:||Sentiment Classification (SC) is about assigning a positive, negative or neutral label to a piece of text based on its overall opinion. This paper describes our in-progress work on extracting the meaning of words for SC. In particular, we investigate the utility of sense-level polarity information for SC. We first show that methods based on common classification features are not robust and their performance varies widely across different domains. We then show that sense-level polarity information features can significantly improve the performance of SC. We use datasets in different domains to study the robustness of the designated features. Our preliminary results show that the most common sense of the words result in the most robust results across different domains. In addition our observation shows that the sense-level polarity information is useful for producing a set of high-quality seed words which can be used for further improvement of SC task. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.|
|Source Title:||AAAI Workshop - Technical Report|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.