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Paper Title

EMOTION DETECTION USING LONG SHORT-TERM MEMORY

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Registration ID: IJNRD_222476

Published ID: IJNRD2405793

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Keywords

Emotion Detection, LSTM, text, Artificial Intelligence.

Abstract

Based on Long Short-Term Memory (LSTM) networks, which are types of Recurrent Neural Networks (RNNs) that are capable of capturing long-range dependencies in sequential data, this study proposes an Emotion Detection framework for movie reviews. The construction of a classifier capable of distinguishing between positive and negative movie review sentiments is the objective here. First and foremost, we collect the dataset from film audits with explanations that are utilized to name the surveys as certain or negative. This dataset structures a reason for preparing and evaluation of our LSTM-Feeling Location model. The proposed structure depends on cleaning the film audit information, tokenizing the message, and encoding it into numeric portrayals that can be taken care of into the LSTM brain organization. The LSTM design is from there on prepared utilizing the marked dataset, and it will become familiar with the subtleties of language and logical conditions which are demonstrative of positive and negative opinions. Subsequent to being prepared, the LSTM model can order concealed film surveys, which can add to the evaluation of how crowds see motion pictures. This system not just empowers ongoing Feeling Location of new film surveys however it likewise offers an incredible asset that can be utilized to disclose patterns and examples in crowd sentiments after some time.

How To Cite (APA)

P S Periasamy Hanumanth & Dr J B Shajilin Loret (May-2024). EMOTION DETECTION USING LONG SHORT-TERM MEMORY. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), h705-h711. https://ijnrd.org/papers/IJNRD2405793.pdf

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Other Publication Details

Paper Reg. ID: IJNRD_222476

Published Paper Id: IJNRD2405793

Research Area: Engineering

Author Type: Indian Author

Country: TIRUNELVELI/TIRUNELVELI TOWN, TAMIL NADU, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405793.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405793

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