Persian Opinion Mining based on Transfer Learning | ||
| مجله مهندسی برق دانشگاه تبریز | ||
| Volume 50, Issue 3 - Serial Number 93, November 2020, Pages 1215-1224 PDF (330.18 K) | ||
| Document Type: Original Article | ||
| Authors | ||
| S. Dehghani1; V. Derhami* 1; A. M. Zare Bidoki2; M. E. Basiri3 | ||
| 1Faculty of Computer Engineering, Yazd University, Yazd, Iran, | ||
| 2Faculty of Computer Engineering, Yazd University, Yazd, Iran | ||
| 3Faculty of Computer Engineering, Shahrekord University, Shahrekord, Iran | ||
| Abstract | ||
| In the past decade, the study of human opinions, feelings and tendencies has been very effective in the decision-making of managers and individuals. Machine learning algorithms play an important role in the field of opinion mining, but they suffer from a big problem: most of the machine learning algorithms assume that the feature dimensions and data distribution are equal, but most of real-world applications don't follow these assumptions. In fact, the data that the algorithm will receive in the future may have different dimensions or distributions. In this article, a new method for improving sentiment analysis of opinions is proposed by the aid of feature-based transfer learning. In the proposed method, initially, the feature or topic of the opinion in the source language domain is identified. Then, by collecting adjectives, adverbs and totally a package of probabilities about that feature and by translating it into the target language, learning from the source language is transferred into the target language. An analysis of the proposed method on the data available at the Amazon store as the source domain indicates that by creating a pattern of feature transferring in English, the polarity of 77% of the opinions in Persian (recorded at the Digikala store) can be extracted that outperforms the SCL, SFA and TCA models with 9, 5 and 5 percent respectively. | ||
| Keywords | ||
| Opinion mining; Transfer learning; Feature transfer; Polarity | ||
| References | ||
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