Equanimous Intelligent Water Drop Algorithm-Based Feed-Forward Neural Network (EIWDA-FFNN) for Improving Sentiment Analysis
Equanimous Intelligent Water Drop Algorithm-Based Feed-Forward Neural Network (EIWDA-FFNN) for Improving Sentiment Analysis |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-9 |
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| Year of Publication : 2023 | ||
| Author : P. Radha, N. Sudha Bhuvaneswari |
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| DOI : 10.14445/22315381/IJETT-V71I9P230 | ||
How to Cite?
P. Radha, N. Sudha Bhuvaneswari, "Equanimous Intelligent Water Drop Algorithm-Based Feed-Forward Neural Network (EIWDA-FFNN) for Improving Sentiment Analysis," International Journal of Engineering Trends and Technology, vol. 71, no. 9, pp. 341-355, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I9P230
Abstract
Sentiment analysis is critical in natural language processing, particularly online shopping. With the rise of e-commerce platforms and social media, customers generate enormous amounts of data through their reviews and feedback. The sentiment analysis of this data provides valuable insights into customer opinions and preferences, enabling companies to improve their products and services. However, sentiment analysis faces several challenges, including the ambiguity of the language used in customer reviews and the lack of labelled data. Researchers have developed various techniques to overcome these challenges, including machine learning and deep learning models. This research paper presents a new approach to sentiment analysis in online shopping reviews, using the Equanimous Intelligent Water Drop Algorithm-based Feed-Forward Neural Network (EIWD-FFNN). The proposed algorithm combines the Equanimous Intelligent Water Drop (EIWD) algorithm with the Feed-Forward Neural Network to optimize the network's hyperparameters for accurate sentiment classification. The EIWD algorithm maximizes the number of hidden layers, the learning rate, and the number of neurons in each hidden layer of the FFNN. This optimization approach reduces the dependency on labelled data and enhances the performance of the sentiment analysis task. The experiments are conducted on a dataset of customer reviews from Amazon. The results demonstrate that the proposed EIWD-FFNN algorithm outperforms other state-of-the-art sentiment analysis techniques, achieving an accuracy of 90.17%. Our study highlights the importance of developing accurate sentiment analysis techniques to understand customer feedback and improve products and services. The proposed algorithm offers a new approach that can overcome the challenges of ambiguous language and lack of labelled data, providing valuable insights into customer opinions. Overall, the proposed EIWD-FFNN algorithm offers a promising approach for sentiment analysis and could be a useful tool for businesses to improve their understanding of customer feedback in online shopping platforms.
Keywords
Sentiment analysis, Classification, Amazon, Neural network, Intelligent water drop, Feed-Forward.
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