TY - JOUR ID - 139378 TI - A Deep Learning Model for Classifying Quality of User Replies JO - International Journal of Web Research JA - IJWR LA - en SN - 2645-4335 AU - Rajabi, Masoumeh AU - Nemati, Shahla AU - Basiri, Mohammad Ehsan AD - Department of Computer Eng., Shahrekord University, Shahrekord, Iran AD - Department of Computer Engineering, Faculty of Engineering Shahrekord University Shahrekord, Iran AD - Computer Engineering Dept., Shahrekord University, Shahrekord, Iran Y1 - 2021 PY - 2021 VL - 4 IS - 1 SP - 18 EP - 26 KW - Text Classification KW - deep neural networks KW - Social Media Text Processing KW - Machine Learning DO - 10.22133/ijwr.2021.288231.1095 N2 - Q&A forums are designed to help users in finding useful information and accessing high-quality content posted by other users in text forums. Automatically identifying high-quality replies posted in response to the initial posts not only provides users with appropriate content, but also saves their time. Existing methods for classifying user replies based on their quality, try to extract quality features from both the textual content and metadata of the replies. This feature engineering step is a time and labor-intensive task. The current study addresses this problem by proposing new model based on deep learning for detecting quality user replies using only raw textual content. Specifically, we propose a long short-term memory (LSTM) model that exploits the embeddings from language models (ELMo) for representing words as contextual numerical vectors. We compared the effectiveness of the proposed model with four traditional machine learning models on the TripAdvisor for New York City (NYC) and the Ubuntu Linux distribution online forums datasets. Experimental results indicated that the proposed model significantly outperformed the four traditional algorithms on both datasets. Moreover, the proposed model achieved about 16% higher accuracy compared to that obtained by the traditional algorithms trained on both textual and quality dimension features. UR - https://ijwr.usc.ac.ir/article_139378.html L1 - https://ijwr.usc.ac.ir/article_139378_b6b4389a31a78e80a4bb79704d637972.pdf ER -