This thesis presents a computational text analysis tool called AFFECTiS (Affect Interpretation/Inference System) which focuses on the task of interpreting natural language text based on its subjective, non-factual, affective properties that go beyond the 'traditional' factual, objective dimensions of meaning that have so far been the main focus of Natural Language Processing and Computational Linguistics. The thesis presents a fully compositional uniform wide-coverage computational model of sentiment in text that builds on a number of fundamental compositional sentiment phenomena and processes discovered by detailed linguistic analysis of the behaviour of sentiment across key syntactic constructions in English. Driven by the Principle of Semantic Compositionality, the proposed model breaks sentiment interpretation down into strictly binary combinatory steps each of which explains the polarity of a given sentiment expression as a function of the properties of the sentiment carriers contained in it and the grammatical and semantic context(s) involved. An initial implementation of the proposed compositional sentiment model is de- scribed which attempts direct logical sentiment reasoning rather than basing compu- tational sentiment judgements on indirect data-driven evidence. Together with deep grammatical analysis and large hand-written sentiment lexica, the model is applied recursively to assign sentiment to all (sub )sentential structural constituents and to concurrently equip all individual entity mentions with gradient sentiment scores. The system was evaluated on an extensive multi-level and multi-task evaluation framework encompassing over 119,000 test cases from which detailed empirical ex- perimental evidence is drawn. The results across entity-, phrase-, sentence-, word-, and document-level data sets demonstrate that AFFECTiS is capable of human-like sentiment reasoning and can interpret sentiment in a way that is not only coherent syntactically but also defensible logically - even in the presence of the many am- biguous extralinguistic, paralogical, and mixed sentiment anomalies that so tellingly characterise the challenges involved in non-factual classification.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:559817 |
| Date | January 2010 |
| Creators | Moilanen, Karo |
| Contributors | Pulman, Stephen |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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