ItemConference Paper

Enhanced Artificial Neural Network for Emoji Sentiment Analysis

Title
Enhanced Artificial Neural Network for Emoji Sentiment Analysis
Creator
Swetha Cordelia, A.; Kokatnoor, Sujatha Arun; Lamani, Manjunath Ramanna; Das, Shreyashi
Description
Emojis enhance textual communication by conveying emotions and providing contextual richness. This study compares the performance of supervised machine learning models such as Naive Bayes, Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANNs) for emoji sentiment classification. A major addition in this study is the enhancement of the ANN model using an informed weight initialization technique, which speeds up convergence and reduces training time while maintaining improved performance. The experimental results showed that the Enhanced ANN (EANN) model obtained 94% accuracy, a 2% improvement over the baseline ANN model, while lowering training time from 45 to 18 units (60% decrease), highlighting the importance of initialization strategies in deep learning. The initialization method helped the EANN network avoid overfitting, resulting in increased generalization and accuracy. Proper initialization balanced the gradients during backpropagation, avoiding gradient issues that limit deep networks. Also, the informed weight initialization guaranteed that the EANN began training closer to an optimal solution, lowering the possibility of becoming confined in suboptimal local minima. The findings from this study contribute to advances in sentiment analysis and text mining, particularly in terms of improving the efficiency and accuracy of deep learning approaches. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Source
Lecture Notes in Networks and Systems;Volume;1672 LNNS;pp.205-217
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Artificial Neural Networks (ANNs); Computational efficiency; Emoji sentiment analysis; He and Xavier initialization; Sentiment classification; Supervised machine learning; Text mining; Weight initialization strategy
Coverage
Swetha Cordelia A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Lamani M.R., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Das S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981953491-3;
Format
online
Language
English
Type
Conference paper

Citation

Swetha Cordelia, A.; Kokatnoor, Sujatha Arun; Lamani, Manjunath Ramanna; Das, Shreyashi, “Enhanced Artificial Neural Network for Emoji Sentiment Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/25442.

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