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Twitter data emotion analysis using Hadoop and metaheuristic optimized Graphical Neural Network.

Xiaohui Wang¹, Yang Li², Fangyuan Chen²

¹School of Big Data, Qingdao Huanghai University, Qingdao, Shandong, China.

Frontiers in Artificial Intelligence

|November 10, 2025

Summary

This study enhances social media emotion analysis using a Graphical Neural Network (GNN) optimized by the Modified Elephant Herd Optimization (MEHO) algorithm. MEHO significantly improves classification accuracy and reduces manual labeling efforts for better sentiment analysis.

Keywords:

Graphical Neural Network Hadoop R-visualization Twitter map reduce movie reviews optimization rolling window

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Area of Science:

Computer Science
Artificial Intelligence
Data Science

Background:

Large-scale unstructured data processing presents challenges for sentiment classification.
Traditional Graphical Neural Networks (GNNs) suffer from suboptimal performance due to manual hyperparameter tuning.
Effective emotion analysis of social media data requires robust and efficient methodologies.

Purpose of the Study:

To apply the Hive framework within the Hadoop ecosystem for sentiment classification of social media data.
To introduce the Modified Elephant Herd Optimization (MEHO) algorithm for optimizing GNNs in sentiment analysis.
To develop an automated dataset construction system and advanced preprocessing techniques to enhance data quality and reduce manual effort.

Main Methods:

Utilized the Hive framework on the Hadoop ecosystem for processing large-scale unstructured data.
Implemented a Graphical Neural Network (GNN) for sentiment categorization of Twitter comments.
Employed the Modified Elephant Herd Optimization (MEHO) algorithm to optimize GNN hyperparameters, weights, and feature subsets.
Integrated Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW) for feature extraction.
Developed an automated dataset construction system and information entropy-based phrase ranking for preprocessing.

Main Results:

The MEHO algorithm reduced premature convergence by 40% and improved classification accuracy by 6.1% compared to the standard EHO algorithm.
The automated labeling system decreased manual labeling effort by 80%.
Entropy-based preprocessing enhanced phrase difficulty classification accuracy by 7%.

Conclusions:

The proposed approach using GNNs optimized with MEHO provides an effective solution for social media emotion analysis.
The automated dataset construction and preprocessing methods significantly improve efficiency and data quality.
Future research directions include multi-modal data fusion and optimizing MEHO for ultra-large feature sets.