Text Mining for Word Sentiment Detection
| Autor: | Susan Gauch, Kevin Labille, Sultan Alfarhood |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Sentiment analysis Probabilistic logic 02 engineering and technology computer.software_genre Lexicon Information theory ComputingMethodologies_ARTIFICIALINTELLIGENCE Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION 020204 information systems 0202 electrical engineering electronic engineering information engineering Unsupervised learning A priori and a posteriori 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing Word (computer architecture) |
| Zdroj: | Communications in Computer and Information Science ISBN: 9783319997001 IC3K |
| DOI: | 10.1007/978-3-319-99701-8_7 |
| Popis: | This work presents a novel approach for automatically generating a sentiment lexicon. We employ an unsupervised learning approach using several probabilistic and information theoretic models. While most of the unsupervised approaches require a set of seed words to begin their work, our methods differ from these by using no a priori knowledge. In addition, our models are effective with a diverse corpus rather than requiring a corpus for a limited domain. We demonstrate the effectiveness of our approaches by performing sentiment analysis on Amazon products reviews, comparing the various automatically-generated lexicons. Based on our cross validation results, we show that our lexicons outperform a widely-used sentiment lexicon on both balanced and unbalanced datasets. |
| Databáze: | OpenAIRE |
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