Comparing Machine Learning Classifiers for Movie WOM Opinion Mining
| Autor: | Yoosin Kim, Seung Ryul Jeong, Do Young Kwon |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Boosting (machine learning)
Artificial neural network Computer Networks and Communications Computer science business.industry Sentiment analysis Decision tree Machine learning computer.software_genre Support vector machine Market research Naive Bayes classifier Business analytics Artificial intelligence business computer Information Systems |
| Zdroj: | KSII Transactions on Internet and Information Systems. 9:3169-3181 |
| ISSN: | 1976-7277 |
| DOI: | 10.3837/tiis.2015.08.025 |
| Popis: | Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naive Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naive Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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