Machine Learning-Based Sentiment Analysis of Incoming Calls on Helpdesk

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Published: Oct 27, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i9.8113
Keywords:
Sentiment analysis, keyword identification, machine learning model, fake call detection

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Chandrakant Deelip Kokane
Nutan Maharashtra Institute of Engineering and Technology, Talegaon, Pune
Kishor R Pathak
Vishwakarma Institute of Information Technology, Pune
Gopal Mohadikar
Sr. Assistant Professor, Tolani Maritime Institute, Induri, Pune, India(MS)
Rakhi Subhash Pagar
Pimpri Chinchwad College of Engineering, Pune
Suhas Chavan
Department of Computer Engineering, Vishwakarma University, Kondhwa, Pune
Sopan Bapu Kshirsagar
Nutan Maharashtra Institute of Engineering and Technology, Talegaon, Pune

Abstract

In today's daily life we are getting so many anonymous calls. Some calls are related to loan marketing and finance. As per the survey, one person is getting 26% spam calls in a day. The proposed methodology accepts user calls and based on the conversation the spam numbers are identified and the same information is provided to the other callers. This is possible because of machine learning-based sentiment analysis. Sentiment analysis is the subdomain of machine learning. The goal of this research is to propose an adaptive methodology for incoming calls. The sentiment-based incoming calls help desk works with freely available lexical resources WordNet, SemCor, and OMSTI. The discussed methodology accepts user conversations in audio format the speech-to-text conversion of the audio will be done. After pre-processing the keyword is detected from the statement. The word2Vec word embedding technique is used for representing words from document space to vector space. The 150-200 dimensional word vector is generated. The WordNet is used for sense mapping and keyword identification. Based on the sentiment analysis of input calls the decision is taken whether to accept or reject calls. This methodology is generating superior results for supervised machine learning models.

Article Details

How to Cite
Kokane, C. D. ., Pathak, K. R. ., Mohadikar, G. ., Pagar, R. S. ., Chavan, S. ., & Kshirsagar, S. B. . (2023). Machine Learning-Based Sentiment Analysis of Incoming Calls on Helpdesk. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 21–27. https://doi.org/10.17762/ijritcc.v11i9.8113
Issue
Vol. 11 No. 9 (2023)
Section
Articles

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