Patterns Recognition of Sentimental Patterns from the X-Dataset using Deep Learning Algorithm

This research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selectio...

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Bibliographic Details
Published in2024 4th International Conference on Soft Computing for Security Applications (ICSCSA) pp. 179 - 183
Main Author Konduri, Praveen S R
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.09.2024
Subjects
Accuracy
Artificial neural networks
Blogs
Deep learning
Deep Neural Network
Optimization
Pattern recognition
PSO
Reviews
Security
Sentiment analysis
Social networking (online)
Online AccessGet full text
DOI10.1109/ICSCSA64454.2024.00036

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Summary:This research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. The study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. The results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. This research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research.
DOI:10.1109/ICSCSA64454.2024.00036