{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-novel-approach-for-enhancing-sentiment","title":"A Novel Approach for Enhancing Sentiment Classification of Persian Reviews Using Convolutional Neural Network and Majority Voting Classifier","arxiv_id":null,"date":"2021-12-17","proceeding":"Digital Transformation and Intelligent Systems Conference 2021 12","authors":["Milad Vazan"],"abstract":"Due to the rapid development of Internet-based applications such as social media, sentiment analysis has become one of the most widely used research areas of natural language processing and an important tool for extracting opinions from texts. Because a huge number of comments and reviews are generated today through social media, and these comments are very significant. However, manually analyzing and summarizing them requires a lot of time and money. Therefore, sentiment analysis has entered the field to organize and analyze them by creating an automated system. In recent years, the use of deep learning in sentiment analysis has shown powerful results. However, creating a model alone may not provide the best predictions and lead to errors such as bias and high variance. To reduce these errors and improve the efficiency of model predictions, combining several models known as ensemble learning may provide better results. Therefore, the main purpose of this article is to create a model based on ensemble learning using Several convolutional neural networks and majority voting classifiers to enhance sentiment analysis in Persian reviews. The proposed model was evaluated on two datasets of reviews of electronic products and movies by 5-fold and 10-fold cross-validation. The results indicate that this new approach increases the efficiency of the sentiment analysis model in Persian language.","url_abs":"https://www.researchgate.net/publication/357150745_A_Novel_Approach_for_Enhancing_Sentiment_Classification_of_Persian_Reviews_Using_Convolutional_Neural_Network_and_Majority_Voting_Classifier","url_pdf":"https://www.researchgate.net/publication/357150745_A_Novel_Approach_for_Enhancing_Sentiment_Classification_of_Persian_Reviews_Using_Convolutional_Neural_Network_and_Majority_Voting_Classifier","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-novel-approach-for-enhancing-sentiment","repo_url":"https://github.com/miladvazan/persian_sentiment_analysis","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"majority-voting-classifier","task_name":"Majority Voting Classifier"},{"task_slug":"persian-sentiment-anlysis","task_name":"Persian Sentiment Analysis"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"methods":[{"method_slug":"1d-cnn","method_name":"1D CNN"},{"method_slug":"deep-ensembles","method_name":"Deep Ensembles"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}