A Neural Network Solution for Collaborative Sentiment Analysis

DOI

10.2991/978-94-6463-252-1_44How to use a DOI?

Keywords

Neural Network; Collaborative Neural Network; Sentiment Extraction; Sigmoid Activation; Collaborative Sentiment Extraction

Abstract

There is a growing need to analyze the contents of ecommerce and micro-blogging platforms in order to determine consumer satisfaction as the number of online forums for providing comments on different features or goods grows. To get a sense of how customers feel about their products, service providers read reviews, both positive and negative, both official and informal. This has led to a plethora of studies aimed at deciphering the writings and gleaning the emotions behind them. However, by relying on tried-and-true techniques for tokenization, lemmatization, and additional sentiment extraction through tagging methods, these approaches overlook a few fundamental truths and lead to underfitting or overfitting issues. As a result, the suggested approach exemplifies several cutting-edge tactics, including differential analysis for tokenization, complicated lemmatization with a significant reduction in processing time, threshold-based sentiment extraction, and subsequent summarization. As a result of this study, 98% accuracy is achieved by the use of improved sigmoid-based neural network activations and a novel technique for weight adjustment in the neural networks.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ravikumar Thallapalli
AU  - G. Narsimha
PY  - 2023
DA  - 2023/11/09
TI  - A Neural Network Solution for Collaborative Sentiment Analysis
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 397
EP  - 415
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_44
DO  - 10.2991/978-94-6463-252-1_44
ID  - Thallapalli2023
ER  -