A Review on Deep Learning Aided Sentiment Analysis for Big Data Human Emotion Recognition

Registration ID: IJNRD_199602

Published ID: IJNRD2306357

DOI: Click Here to Get

Krati Gupta , Mahesh Parmar

CNN, RNN, Machine learning, Deep learning, Sentiment Analysis

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).

Published Paper   E-Certificate

Krati Gupta & Mahesh Parmar (June-2023). A Review on Deep Learning Aided Sentiment Analysis for Big Data Human Emotion Recognition . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(6), d558-d565. https://ijnrd.org/papers/IJNRD2306357.pdf

Volume 8 Issue 6, June-2023

Pages : d558-d565

Paper Reg. ID: IJNRD_199602

Published Paper Id: IJNRD2306357

Downloads: 000121976

Research Area: Computer Science & Technology 

Country: Gwalior, Madhya Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2306357.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2306357

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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