Genetic Programming for Domain Adaptation in Product Reviews
| Autor: | Iti Chaturvedi, Roy E. Welsch, Sandro Cavallari, Erik Cambria |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Deep learning Feature extraction Sentiment analysis Genetic programming 02 engineering and technology Genetic program Machine learning computer.software_genre Tree (data structure) Kernel (linear algebra) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Sentence Word (computer architecture) |
| Zdroj: | CEC |
| Popis: | There is a large variety of products sold online and the websites are in several languages. Hence, it is desirable to train a model that can predict sentiments in different domains simultaneously. Previous authors have used deep learning to extract features from multiple domains. Here, each word is represented by a vector that is determined using co-occurrence data. Such a model requires that all sentences have the same length resulting in low accuracy. To overcome this challenge, we model the features in each sentence using a variable length tree called a Genetic Program. The polarity of clauses can be represented using mathematical operators such as ’+’ or ’-’ at internal nodes in the tree. The proposed model is evaluated on Amazon product reviews for different products and in different languages. We are able to outperform the accuracy of baseline multi-domain models in the range of 5–20%. |
| Databáze: | OpenAIRE |
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