Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICTAI.2013.62
Title: Aspect-based Twitter sentiment classification
Authors: Lek, H.H.
Poo, D.C.C. 
Keywords: Aspect-based Sentiment Analysis
Opinion Mining
Sentiment Analysis
Twitter Sentiment Analysis
Issue Date: 2013
Citation: Lek, H.H.,Poo, D.C.C. (2013). Aspect-based Twitter sentiment classification. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI : 366-373. ScholarBank@NUS Repository. https://doi.org/10.1109/ICTAI.2013.62
Abstract: Due to the popularity of Twitter, sentiment classification for Twitter has become a hot research topic. Previous studies have approached the problem as a tweet-level classification task where each tweet is classified as positive, negative or neutral. However, getting an overall sentiment might not be useful to organizations which are using twitter for monitoring consumer opinion of their products/services. Instead, it is more useful to determine specifically which aspects of the products/services the users are happy or unhappy about. This paper proposes an aspect-based sentiment classification approach to analyze sentiments for tweets. To the best of our knowledge, we are the first to perform sentiment analysis for Twitter in this manner. We conducted several experiments and show that by incorporating results from the aspect-based sentiment classifier, we are able to improve existing tweet-level classifiers. The experimental results also demonstrated that our approach outperforms existing state-of-the-art approaches. © 2013 IEEE.
Source Title: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
URI: http://scholarbank.nus.edu.sg/handle/10635/78027
ISBN: 9781479929719
ISSN: 10823409
DOI: 10.1109/ICTAI.2013.62
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

13
checked on Oct 23, 2018

Page view(s)

62
checked on Sep 28, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.