Study of Aspect level Sentiment Analysis and Word Embeddings
| Autor: | Milind B. Waghmare, Gaurav Sharma |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Word embedding
business.industry Computer science Sentiment analysis Probabilistic logic 02 engineering and technology computer.software_genre Aspect detection 020204 information systems 0202 electrical engineering electronic engineering information engineering Task analysis Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence business Hidden Markov model computer Natural language processing Word (computer architecture) |
| Zdroj: | 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). |
| DOI: | 10.1109/icecct.2019.8869404 |
| Popis: | Reviews play critical part in decision making of consumers. It’s becoming tedious for consumers to go through an ever-increasing number of reviews. Hence, it is becoming more important to analyze all these reviews and present in easily understandable format. User is unable to comprehend the essence of all these reviews from average ratings and also, they are unsuitable for providing information related to various features of that entity. Aspect or feature level sentiment analysis captures sentiments of features related to entities providing quick overview of each aspect of entities. This paper studies various methods and approaches for aspect detection and corresponding sentiment analysis. Also, various challenges faced while both sentiment analysis and aspect detection, and their approaches are discussed. Word embeddings for efficient sentiment analysis are also investigated. A system is proposed which uses machine learning as well as word embedding for aspect detection and corresponding sentiment analysis. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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