[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117959-en":3,"doc-seo-117959-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117959,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning-Based Sentiment Analysis of Incoming Calls on Helpdesk","Daily life involves frequent anonymous calls, including a significant share of spam and deceptive finance/loan-related numbers. The proposed methodology processes helpdesk incoming user calls by converting audio to text, performing preprocessing, and detecting relevant keywords. It applies word2Vec embeddings to map words into vector space and uses WordNet for sense mapping and keyword identification. Sentiment analysis then supports an adaptive decision to accept or reject calls, improving performance over supervised machine learning approaches for fake call detection.","Machine Learning-Based Sentiment Analysis of Incoming Calls on Helpdesk  \nDr. Chandrakant Deelip Kokane1, Kishor R Pathak2, Gopal Mohadikar3, Rakhi Subhash Pagar4, Suhas Chavan5, Sopan  \nBapu Kshirsagar6  \n1Nutan Maharashtra Institute of Engineering and Technology, Talegaon, Pune  \nORCID: 0000-0001-7957-3933  \n[Email:cdkokane1992@gmail.com](Email:cdkokane1992@gmail.com)  \n2Vishwakarma Institute of Information Technology, Pune  \nORCID: 0000-0001-8409-7433  \n[Email: kishor.pathak@viit.ac.in](Email: kishor.pathak@viit.ac.in)  \n3Sr. Assistant Professor, Tolani Maritime Institute, Induri, Pune, India(MS) .  \nORCID:0009-0007-9677-8931  \nEmail: [gmohadikar@gmail.com](gmohadikar@gmail.com)  \n4Pimpri Chinchwad College of Engineering, Pune  \nORCID: 0009-0000-4306-4483  \n[Email:rakhi.pagar197@gmail.com](Email:rakhi.pagar197@gmail.com)  \n5Department of Computer Engineering, Vishwakarma University, Kondhwa, Pune  \nORCID:0000-0002-9547-0675  \n[Email:chavan.suhas18@gmail.com/suhas.chavan@vupune.ac.in](Email:chavan.suhas18@gmail.com/suhas.chavan@vupune.ac.in)  \n6Nutan Maharashtra Institute of Engineering and Technology, Talegaon, Pune  \nORCID ID:0009-0006-0683-5369  \n[Email:sopankshirsagar02@gmail.com](Email:sopankshirsagar02@gmail.com)  \nAbstract: 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  \nKeyword: Sentiment analysis, keyword identification, machine learning model, fake call detection.  \nI. Introduction  \nFake calls, a seemingly innocuous modern-day phenomenon, hold the potential to exert a considerable influence on individuals and society as a whole. As shown in figure 1.1 the category of calls with respect to the sentiment is proposed here. These simulated or misleading phone calls, often initiated with deceptive intent, can have far-reaching effects on various aspects of life [1] . In this exploration, we delve into the  \nmultifaceted impact of fake calls, encompassing not only the direct consequences on personal and professional realms but also the broader implications for trust, communication, and technological advancement. Understanding these effects is essential to develop strategies that mitigate the negative consequences and promote a more informed and resilient society [2] .  \nFigure 1.1 Machine Learning-Based Sentiment Analysis  \nSentiment analysis is a powerful application of machine learning that involves analyzing and interpreting subjective information from text data to decide the emotion expressed within it. The goal is to categorize the sentiment as positive, negative, or neutral, providing valuable insights into fake phone call detection [3] .  \nTable 1.1 shows the impact of fake phone calls on the files of human beings  \nTable 1.1 Impact of Fake Calls on human being  \n\n| Impact of Fake\u003Cbr>Calls | Descripti","cbCaifANGztHdpca","https://ap.wps.com/l/cbCaifANGztHdpca","pdf",382260,1,7,"English","en",105,"# Abstract\n# Introduction\n## Impact of Fake Calls\n## Sentiment Analysis for Fake Call Detection","[{\"question\":\"How does the helpdesk system process incoming calls in the proposed approach?\",\"answer\":\"The system accepts incoming user conversations in audio form, converts audio to text via speech-to-text, then performs preprocessing and keyword detection for further sentiment analysis.\"},{\"question\":\"Which techniques are used for representing and understanding words?\",\"answer\":\"The approach uses word2Vec word embedding to represent words as 150–200 dimensional vectors, and it applies WordNet for sense mapping and keyword identification.\"},{\"question\":\"What is the decision logic based on sentiment analysis?\",\"answer\":\"Based on the sentiment analysis of the input calls, the system decides whether to accept or reject the calls, targeting improved fake call detection performance.\"}]","Machine Learning-Based Sentiment Analysis of Incoming Calls on Helpdesk | 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does the helpdesk system process incoming calls in the proposed approach?","Question",{"text":75,"@type":76},"The system accepts incoming user conversations in audio form, converts audio to text via speech-to-text, then performs preprocessing and keyword detection for further sentiment analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which techniques are used for representing and understanding words?",{"text":80,"@type":76},"The approach uses word2Vec word embedding to represent words as 150–200 dimensional vectors, and it applies WordNet for sense mapping and keyword identification.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the decision logic based on sentiment analysis?",{"text":84,"@type":76},"Based on the sentiment analysis of the input calls, the system decides whether to accept or reject the calls, targeting improved fake call detection 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