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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: | A Review on Deep Learning Aided Sentiment Analysis for Big Data Human Emotion Recognition |
| Authors Name: | Krati Gupta , Mahesh Parmar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_199602
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| Published Paper Id: | IJNRD2306357 |
| Published In: | Volume 8 Issue 6, June-2023 |
| DOI: | |
| Abstract: | Sentimental and emotional recognition has developed into a crucial study area that can demonstrate a number of practical inputs. A few of the outward manifestations of emotion include speech, gestures, writing, and facial expressions. The issue of emotion recognition within text documents can be solved by combining deep learning principles with natural language processing (NLP). This research also suggests deep learning aided semantic textual analysis (DLSTA) for big data human’s emotion detection. Finding the central idea of a document is done using sentiment analysis. People question if the majority of attendance at an event had a great or negative experience when they post comments about it on social media. Sentiment analysis gathers unstructured textual comments, postings, and images from across all comments shared by various individuals and classifies them as neutral, negative, and positive. Observing how consumers react and utilizing their analysis to motivate product or maintenance staff is a technique known as emotional analyzation through facial movements. This study's main goal was to build a classifier that would choose features from just a real-time image and video dataset while also extracting hybrid features. Recurrent neural networks (RNN) or convolutional neural networks (H-CNN), 2 machine learning classification methods, were used to predict the appropriate sentiment (RNN). |
| Keywords: | CNN, RNN, Machine learning, Deep learning, Sentiment Analysis |
| Cite Article: | "A Review on Deep Learning Aided Sentiment Analysis for Big Data Human Emotion Recognition ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 6, page no.d558-d565, June-2023, Available :http://www.ijnrd.org/papers/IJNRD2306357.pdf |
| Downloads: | 000118757 |
| 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:IJNRD2306357 Registration ID: 199602 Published In: Volume 8 Issue 6, June-2023 DOI (Digital Object Identifier): Page No: d558-d565 Country: Gwalior, Madhya Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2306357 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2306357 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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