Aspect Extraction from Reviews Using Convolutional Neural Networks and Embeddings
| Autor: | Georgios Kontonatsios, Nikolaos Bessis, Peiman Mamani Barnaghi, Yannis Korkontzelos |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Deep learning Sentiment analysis 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Convolutional neural network SemEval Domain (software engineering) Task (project management) 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence Layer (object-oriented design) business computer |
| Zdroj: | Natural Language Processing and Information Systems-24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, Salford, UK, June 26–28, 2019, Proceedings Natural Language Processing and Information Systems ISBN: 9783030232801 NLDB Lecture Notes in Computer Science Lecture Notes in Computer Science-Natural Language Processing and Information Systems |
| ISSN: | 0302-9743 1611-3349 |
| DOI: | 10.1007/978-3-030-23281-8_37 |
| Popis: | Aspect-based sentiment analysis is an important natural language processing task that allows to extract the sentiment expressed in a review for parts or aspects of a product or service. Extracting all aspects for a domain without manual rules or annotations is a major challenge. In this paper, we propose a method for this task based on a Convolutional Neural Network (CNN) and two embedding layers. We address shortcomings of state-of-the-art methods by combining a CNN with an embedding layer trained on the general domain and one trained the specific domain of the reviews to be analysed. We evaluated our system on two SemEval datasets and compared against state-of-the-art methods that have been evaluated on the same data. The results indicate that our system performs comparably well or better than more complex systems that may take longer to train. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|