Univrsity Of Tehran Press Journal of Business Management 2008-5907 14 4 2022 12 22 Customer Reviews Classification with Text Mining and Deep Learning Approach (Case Study: Digikala Customers Reviews) Customer Reviews Classification with Text Mining and Deep Learning Approach (Case Study: Digikala Customers Reviews) 675 694 90594 10.22059/jibm.2022.334338.4255 FA Parham Parnian Msc. Student, Department of Computer Engineering, Faculty of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. Journal Article 2021 12 06 <strong>Objective</strong> Today, people face different decision-making criteria when purchasing products and services. One of these criteria is using the reviews of the previous purchasers of products and services. A large volume of reviews is seen as a challenge for these people. The present study aimed to create a model to analyze users’ sentiments and to classify their reviews to solve the mentioned challenge. <strong>Methodology</strong> The present study investigated the buyers’ reviews of mobile phones purchased on the Digikala Website from 2015 to 2016. To analyze the sentiments, and to classify the reviews, deep learning-based algorithms, and convolutional networks, subtypes of deep networks, were suggested. Prior to preprocessing and homogenizing the data, the study used a pre-trained Fastext model to convert the words into integer vectors and deliver them as inputs to the proposed deep network. <strong>Findings</strong> To train the selected model, the training algorithm was carried out on it 90 times. To validate the performance of the selected model, confusion matrix, accuracy, recall, F1-score, and precision rate criteria were used. <strong>Conclusion</strong> The present study used the deep networks approach, convolutional networks, and bidirectional long short-term memory to classify the buyers’ reviews of the mobile phone from the website above at 93% accuracy, and after 90 training periods. <strong>Objective</strong> Today, people face different decision-making criteria when purchasing products and services. One of these criteria is using the reviews of the previous purchasers of products and services. A large volume of reviews is seen as a challenge for these people. The present study aimed to create a model to analyze users’ sentiments and to classify their reviews to solve the mentioned challenge. <strong>Methodology</strong> The present study investigated the buyers’ reviews of mobile phones purchased on the Digikala Website from 2015 to 2016. To analyze the sentiments, and to classify the reviews, deep learning-based algorithms, and convolutional networks, subtypes of deep networks, were suggested. Prior to preprocessing and homogenizing the data, the study used a pre-trained Fastext model to convert the words into integer vectors and deliver them as inputs to the proposed deep network. <strong>Findings</strong> To train the selected model, the training algorithm was carried out on it 90 times. To validate the performance of the selected model, confusion matrix, accuracy, recall, F1-score, and precision rate criteria were used. <strong>Conclusion</strong> The present study used the deep networks approach, convolutional networks, and bidirectional long short-term memory to classify the buyers’ reviews of the mobile phone from the website above at 93% accuracy, and after 90 training periods. https://jibm.ut.ac.ir/article_90594_e4b76ad7f079c3ad7bbc3b0f429e7c1d.pdf