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Title: Computational modelling of word sense sentiment
Author: Su, Fangzhong
ISNI:       0000 0004 2746 2248
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2010
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In recent years, sentiment analysis which employs computational models to tackle opin- ion, attitude, emotion or judgement in text has become an important discipline in the area of natural language processing. It also has great potential in real world applications, ranging from personal decisions such as recognizing customer opinions in movie or hotel service reviews, to industry or government concerns such as analyzing user feedback on new products or tracking the public's reaction to national or international events. The main objective of this thesis is to investigate the interaction between word sense ambiguity and sentiment analysis. Towards this goal, it validates three key hypotheses: (1) sentiment can be assigned to word senses by humans, and sentiment assignment at the word sense level is also more reliable than at the word level; (2) word sense sentiment can be assigned by automatic algorithms with high accuracy and limited training data; and (3) word sense sentiment can improve word translation. This work begins with an investigation of the reliability of manual subjectivity and polarity label assignment on word senses. High agreement is gained from the human annotation study, indicating that subjectivity and polarity labelling on word senses is a well-defined task and should be suitable for automatic learning as well. Then various ma- chine learning approaches including heuristic unsupervised learning, supervised learning, and graph-based semi-supervised learning are proposed to automatically determine word sense sentiment. The experimental results show the effectiveness of all the three learn- ing models. In particular, for word sense subjectivity classification, the proposed semi- supervised graph-cut approach significantly outperforms the unsupervised heuristic-based approach, the supervised approach, as well as all prior competing approaches proposed by other researchers. We then automatically generate a complete subjectivity lexicon of more than 110,000 word senses by the semi-supervised graph-cut approach. Lastly, the poten- tial application of word sense sentiment information in cross-lingual lexical substitution is also explored. We posit a new assumption that good word substitutions will transfer a word's contextual sentiment from the source language into the target language. In prac- tice, to test this assumption, the word sense subjectivity information is incorporated as an additional feature in a system for English-Chinese lexical substitution. The usefulness of word sense sentiment information is then confirmed by experiments, as the incorpora- tion of subjectivity information yields significant improvement over a sentiment-unaware system.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available