Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
SENTIMENT ASSESSMENT OF TEXT BY ANALYZING LINGUISTIC FEATURES AND CONTEXTUAL VALENCE ASSIGNMENT
Applied Artificial Intelligence
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
What's with the attitude?: identifying sentences with attitude in online discussions
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Aspect-based sentiment analysis of movie reviews on discussion boards
Journal of Information Science
Towards a micro-blog platform for sensing and easing adolescent psychological pressures
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Recent sentiment analysis research has focused on the functional relations of words using typed dependency parsing as this provides a refined analysis on the grammar and semantics of the textual data, which could improve performance. However, typed dependencies only provide the grammatical relationships between individual words while there exist more complex relationships between words that could influence a sentence sentiment polarity. In this paper, we propose a linguistic approach, called Polarity Prediction Model (PPM), that combines typed dependencies and subjective phrase analysis to detect sentence-level sentiment polarity. Our approach also considers the intensity of words and domain terms that could influence the sentiment polarity output. PPM is shown to provide a fine-grained analysis for handling and explaining the complex relationships between words in detecting a sentence sentiment polarity. PPM was found to consistently outperform a baseline model by 5% in terms of overall F1-score, and exceeding 10% in terms of positive F1- score when compared to a Typed-dependency only approach.