{"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/auto-analysis-of-customer-feedback-using-cnn","title":"Auto Analysis of Customer Feedback using CNN and GRU Network","arxiv_id":"1710.04600","date":"2017-10-12","proceeding":null,"authors":["Deepak Gupta","Pabitra Lenka","Harsimran Bedi","Asif Ekbal","Pushpak Bhattacharyya"],"abstract":"Analyzing customer feedback is the best way to channelize the data into new\nmarketing strategies that benefit entrepreneurs as well as customers. Therefore\nan automated system which can analyze the customer behavior is in great demand.\nUsers may write feedbacks in any language, and hence mining appropriate\ninformation often becomes intractable. Especially in a traditional\nfeature-based supervised model, it is difficult to build a generic system as\none has to understand the concerned language for finding the relevant features.\nIn order to overcome this, we propose deep Convolutional Neural Network (CNN)\nand Recurrent Neural Network (RNN) based approaches that do not require\nhandcrafting of features. We evaluate these techniques for analyzing customer\nfeedback sentences in four languages, namely English, French, Japanese and\nSpanish. Our empirical analysis shows that our models perform well in all the\nfour languages on the setups of IJCNLP Shared Task on Customer Feedback\nAnalysis. Our model achieved the second rank in French, with an accuracy of\n71.75% and third ranks for all the other languages.","url_abs":"http://arxiv.org/abs/1710.04600v1","url_pdf":"http://arxiv.org/pdf/1710.04600v1.pdf","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":"auto-analysis-of-customer-feedback-using-cnn","repo_url":"https://github.com/pabitralenka/Customer-Feedback-Analysis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"marketing","task_name":"Marketing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}