A Web Search Enhanced Feature Extraction Method for Aspect-Based Sentiment Analysis for Turkish Informal Texts
| Autor: | Batuhan Kama, Pinar Karagoz, Ismail Hakki Toroslu, Murat Ozturk, Ozcan Ozay |
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| Rok vydání: | 2016 |
| Předmět: |
Web search query
Turkish business.industry Computer science Feature extraction Sentiment analysis 02 engineering and technology computer.software_genre Latent Dirichlet allocation language.human_language Noun phrase symbols.namesake 020204 information systems Online search 0202 electrical engineering electronic engineering information engineering language symbols 020201 artificial intelligence & image processing Artificial intelligence business Precision and recall computer Natural language processing |
| Zdroj: | Big Data Analytics and Knowledge Discovery ISBN: 9783319439457 DaWaK |
| DOI: | 10.1007/978-3-319-43946-4_15 |
| Popis: | In this article, a new unsupervised feature extraction method for aspect-based sentiment analysis is proposed. This method improves the performance of frequency based feature extraction by using an online search engine. Although frequency based feature extraction methods produce good precision and recall values on formal texts, they are not very successful on informal texts. Our proposed algorithm takes the features of items suggested by frequency based feature extraction methods, then, eliminates the features which do not co-occur with the item, whose features are sought, on the Web. Since the proposed method constructs the candidate feature set of the item from the Web, it is domain-independent. The results of experiments reveal that for informal Turkish texts, much higher performance than frequency based method is achieved. |
| Databáze: | OpenAIRE |
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