| Classification of Persian Product Reviews Using Neural Networks |
| کد مقاله : 1034-AISCH2-FULL |
| نویسندگان |
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کریم اخوان آذری *1، محمد بحرانی2 1دانشگاه صنعتی شریف 2استاد گروه رایانه دانشکده علوم ریاضی و رایانه دانشگاه علامه طباطبائی |
| چکیده مقاله |
| With the rising influence of reviews on online retail shopping, automated opinion mining of consumer reviews is becoming increasingly important. Opinion mining or the classification of reviews is done using machine learning algorithms or neural networks, yet works in this area for the Persian language are limited. This paper tries to implement and demonstrate the performance of three neural networks by training them on the product reviews dataset from DigiKala, an Iranian e-commerce company. This work proposes three different RNN-based models as Multi-label text classifiers for the classification of product review documents. This experiment has three different stages which are preprocessing, training, and performance evaluation. The preprocessing stage involves different subtasks. An LSTM and a BiLSTM model are used and an RNN is used as the baseline to show how effective the aforementioned models are at classifying the samples, which the BiLSTM model shows slightly better results than the other two. |
| کلیدواژه ها |
| Opinion Mining, Text classification, Sentiment analysis, Deep learning |
| وضعیت: پذیرفته شده برای ارائه شفاهی |