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Semantic sentiment analysis of twitter

Saif, Hassan; He, Yulan and Alani, Harith (2012). Semantic sentiment analysis of twitter. In: The 11th International Semantic Web Conference (ISWC 2012), 11-15 November 2012, Boston, MA, USA.

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Abstract

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics’ feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. “Apple product”) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

Item Type: Conference Item
Copyright Holders: Not known
Project Funding Details:
Funded Project NameProject IDFunding Body
ROBUST257859EU-FP7
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 34929
Depositing User: Kay Dave
Date Deposited: 31 Oct 2012 11:20
Last Modified: 12 Oct 2013 16:21
URI: http://oro.open.ac.uk/id/eprint/34929
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