Emotions-based Sentiment Analysis using Fusion-Based Learning Model
Emotions-based Sentiment Analysis using Fusion-Based Learning Model |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-6 |
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| Year of Publication : 2023 | ||
| Author : Penubaka Balaji, D. Haritha, Konatham Sumalatha, Uma Priya D, Rudramani Bhutia, Rahul Kumar |
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| DOI : 10.14445/22315381/IJETT-V71I6P206 | ||
How to Cite?
Penubaka Balaji, D. Haritha, Konatham Sumalatha, Uma Priya D, Rudramani Bhutia, Rahul Kumar, "Emotions-based Sentiment Analysis using Fusion-Based Learning Model," International Journal of Engineering Trends and Technology, vol. 71, no. 6, pp. 47-53, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I6P206
Abstract
Emotions-based sentiment analysis using fusion-based learning models uses artificial intelligence (AI) techniques to identify and analyze emotions associated with a particular text or speech. This approach involves using machine learning (ML) algorithms to fuse multiple sources of information, including linguistic features, acoustic features, and contextual information, to determine the sentiment of a particular text or speech. The aim of the proposed approach is utilized to find accurate sentiments by combining several models. The fusion-based learning model (FBLM) combines various data types to better represent the sentiment expressed in the text or speech. Several steps are involved in the FBLM approach, including data preparation, extracting features, combining features, and emotions classification. In the data preprocessing stage, the input data is cleaned and standardized to remove irrelevant information. In the feature extraction step, linguistic, acoustic, and contextual features are extracted from the text or speech. In the feature fusion step, the extracted features are combined using various fusion techniques to create a more comprehensive representation of the sentiment expressed in the text or speech. Finally, in the sentiment classification step, linear regression (LR) is used to classify the opinion of the text or speech. The proposed approach has several advantages over traditional sentiment analysis techniques, including higher accuracy, more comprehensive sentiment analysis, and the ability to analyze emotions associated with specific words or phrases. The approach has potential applications in various fields, including analysis of social media (ASM), user comments analysis, and online market analysis.
Keywords
Machine Learning (ML), Sentiment Analysis (SA), Fusion-based learning, Classification.
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