ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1672252
Twitter Data Emotion Analysis Using Hadoop and Metaheuristic Optimized Graphical Neural Network
Provisionally accepted- 1Qingdao Huanghai University, Qingdao, China
- 2Shandong University of Science and Technology, Qingdao, China
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This study applies the Hive framework within the Hadoop ecosystem for sentiment classification, focusing on emotion analysis of X data. After outlining Hadoop's core advantages in large-scale unstructured data processing, the study focuses on using a Graphical Neural Network (GNN) for sentiment categorization of Twitter comments. To address the suboptimal performance of traditional GNNs due to trial-and-error hyperparameter tuning, the study introduces the Modified Elephant Herd Optimization (MEHO) algorithm - improved version of the standard EHO, to optimize the network's weight parameters, hyperparameters, and feature subsets, ensuring a balance between exploration and exploitation. An automated dataset construction system has also been developed to reduce manual labeling effort and ensure consistency. Preprocessing techniques, including information entropy–based phrase ranking, further enhance data quality. To capture both semantic and statistical features of tweets, feature extraction methods such as Term Frequency–Inverse Document Frequency (TF-IDF) and Bag of Words (BoW) are integrated. Experimental results demonstrate that MEHO reduces premature convergence by 40% and improves classification accuracy by 6.1% compared with the standard EHO algorithm. The automated labeling system decreases manual effort by 80%, while entropy-based preprocessing increases phrase difficulty classification accuracy by 7%. This study provides an effective solution for social media emotion analysis; future research will explore multi-modal data fusion and optimize MEHO's convergence speed for ultra-large feature sets.
Keywords: hadoop, R-visualization, Twitter, Movie reviews, optimization, Graphical Neural Network, rolling window, Map Reduce
Received: 24 Jul 2025; Accepted: 06 Oct 2025.
Copyright: © 2025 Wang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yang Li, dreyang@163.com
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