机器学习和深度学习情感分析模型:墨西哥西班牙语推文 SENT-COVID 语料库案例研究

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Informatics Pub Date : 2024-04-23 DOI:10.3390/informatics11020024
Helena Gómez-Adorno, G. Bel-Enguix, Gerardo Sierra, Juan-Carlos Barajas, William Álvarez
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引用次数: 0

摘要

本文全面评估了传统机器学习和深度学习模型在分析 SENT-COVID Twitter 语料库中的情感趋势方面的效果,该语料库是在 COVID-19 大流行期间制作的。该语料库由 COVID-19 相关关键词过滤并人工标注极性,是进行情感分析实验的重要资源。我们的研究调查了各种方法,包括基于向量的经典系统(如 word2vec、doc2vec)和各种短语建模技术,以及西班牙文预训练 BERT 模型。我们评估了面向 Python 用户的现成情感分析库的性能,包括 TextBlob、VADER 和 Pysentimiento。此外,我们还实施并评估了逻辑回归、奈夫贝叶斯、支持向量机等传统分类算法以及多层感知器等简单神经网络。在整个研究过程中,我们探索了不同的降维技术。通过这种方法,我们对各种分类方法进行了精确比较,在测试集上,BETO-uncased 的准确率最高,达到 0.73。我们的研究结果强调了传统机器学习和深度学习模型在低资源西班牙语场景和 COVID-19 等新兴话题中分析情感趋势的有效性和适用性。
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Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish
This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.
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来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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