情感分析中分类方法的比较分析:特征选择和集合技术优化的影响

Sarjon Defit, A. Windarto, Putrama Alkhairi
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引用次数: 0

摘要

优化分类方法(前向选择、后向消除和优化选择)和集合技术(AdaBoost 和 Bagging)对于准确的情感分析至关重要,尤其是在社交媒体的政治背景下。本研究比较了高级分类模型和标准分类模型(决策树、随机树、奈夫贝叶斯、随机森林、K- NN、神经网络和广义线性模型),分析了 2023 年 12 月 10-11 日的 1200 条推文,重点关注 "印度尼西亚 "和 "capres"。其中包括 490 条正面情绪、355 条负面情绪和 353 条中性情绪,反映了人们对总统候选人和政治问题的不同看法。增强型模型的准确率达到 96.37%,其中后向选择模型对负面情绪的准确率达到 100%。该研究建议进一步探索混合特征选择和改进分类器,用于高风险情绪分析。通过前向特征选择和集合方法,Naive Bayes 在负面情绪分类方面表现突出,同时保持了较高的总体准确率(96.37%)。
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Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization
Optimizing classification methods (forward selection, backward elimination, and optimized selection) and ensemble techniques (AdaBoost and Bagging) are essential for accurate sentiment analysis, particularly in political contexts on social media. This research compares advanced classification models with standard ones (Decision Tree, Random Tree, Naive Bayes, Random Forest, K- NN, Neural Network, and Generalized Linear Model), analyzing 1,200 tweets from December 10-11, 2023, focusing on "Indonesia" and "capres." It encompasses 490 positive, 355 negative, and 353 neutral sentiments, reflecting diverse opinions on presidential candidates and political issues. The enhanced model achieves 96.37% accuracy, with the backward selection model reaching 100% accuracy for negative sentiments. The study suggests further exploration of hybrid feature selection and improved classifiers for high-stakes sentiment analysis. With forward feature selection and ensemble method, Naive Bayes stands out for classifying negative sentiments while maintaining high overall accuracy (96.37%).
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审稿时长
24 weeks
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