A tool for polarity classification of human affect from panel group texts
| Autor: | Manfred Klenner, Angela Fahrni, Stefanos Petrakis |
|---|---|
| Přispěvatelé: | University of Zurich, Klenner, M |
| Rok vydání: | 2009 |
| Předmět: |
1707 Computer Vision and Pattern Recognition
Computer science Polarity (physics) media_common.quotation_subject Customer reviews 410 Linguistics 1702 Artificial Intelligence 02 engineering and technology 000 Computer science knowledge & systems computer.software_genre Lexicon Social group 1709 Human-Computer Interaction 0202 electrical engineering electronic engineering information engineering media_common Group (mathematics) business.industry 05 social sciences 050301 education Linguistics Focus (linguistics) 1712 Software Feeling 10105 Institute of Computational Linguistics 020201 artificial intelligence & image processing Affect (linguistics) Artificial intelligence business 0503 education computer Natural language processing |
| Zdroj: | ACII |
| DOI: | 10.5167/uzh-19795 |
| Popis: | We introduce an explorative tool for affect analysis from texts. Rather than the full range of emotions, feelings, and sentiment, our system is currently restricted to the positive or negative polarity of phrases and sentences. It analyses the input texts with the aid of a affect lexicon that specifies among others the prior polarity (positive or negative) of words. A chunker is used to determine phrases that are the basis for a compositional treatment of phraselevel polarity assignment. In our current experiments we focus on phrases that are targeted towards persons, be it the writer (I, my, me,.), the social group including the writer (we, our,.) or the reader (you, your,). We evaluate our system with standard data (customer reviews). We also give initial results from a small corpus of 35 texts taken from a panel group called 'I battle depression'. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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