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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Sentiment Analysis on Voice data using Deep Learning |
| Authors Name: | DEEPA YOGISH , Asha N , Yogish H k , Abhishek K |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_181048
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| Published Paper Id: | IJNRD2204094 |
| Published In: | Volume 7 Issue 4, April-2022 |
| DOI: | http://doi.one/10.1729/Journal.29980 |
| Abstract: | Sentiment Analysis (SA) is an application of Natural Language Processing (NLP) that is used to identify and extract sensitive information, such as a person's perspective on a particular piece of text. SA's main idea is to divide the authors' ideas on a variety of topics into positive, negative, or neutral categories. It works in a variety of domains including Business Intelligence (BI), politics, social science, etc. In recent years we have seen an increase in social networking websites, microblogs, and Web applications with a significant increase in user-generated data for sentiment mining. Data from online posts, tweets, videos, etc., all specific reviews on diverse subjects and events, provide big possibilities to have a look at and examine human opinions and sentiment. In this paper, we study how the sentiment of a voice data of a human can be classified into sentiment i.e., emotions possessed by the user while speaking. Emotions divided by the model are neutral, calm, happy, sad, angry, nervous, disgusted, and surprised. Our model captured and analyzed users' emotions and used them to improve user interaction / experience. The models for emotional recognition suggested here are based on the Deep Learning (DL) and Convolution Neural Networks (CNN). The main idea is to consider the Mel-Frequency Cepstral Coefficients (MFCC) normally referred to as the "spectrum of a spectrum", to be the only feature used for training the model. |
| Keywords: | Deep learning, Machine learning, MFCC, Convolution Neural Network, classifications, RAVDESS, TESS, Emotion detection |
| Cite Article: | "Sentiment Analysis on Voice data using Deep Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 4, page no.778-782, April-2022, Available :http://www.ijnrd.org/papers/IJNRD2204094.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2204094 Registration ID: 181048 Published In: Volume 7 Issue 4, April-2022 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.29980 Page No: 778-782 Country: BANGALORE, KARNATAKA, INDIA Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2204094 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2204094 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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