Machine Learning Strategies to Analyze Positive or Negative Sentiments in Twitter Texts

Matheus Santos Da Silva, Alisson Rodrigo Santana dos Santos, Charles Vilela de Souza, Cleyton Rodrigues
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Abstract

The task of analyzing people’s emotions and feelings is known as Sentiment Analysis (SA). Currently, several techniques have been used together for extracting and detecting feelings, such as Natural Language Processing (NLP) and Machine Learning (ML) algorithms. The present work aims to reuse and evaluate an intelligent model to analyze positives or negatives in Portuguese texts on Twitter, considering that these feelings can be indicators of depression. Therefore, we have used ML algorithms together with SA and NLP techniques, resulting in an accuracy of 79%.
机器学习策略分析推特文本中的积极或消极情绪
分析人们的情绪和感受的任务被称为情绪分析(SA)。目前,有几种技术被用于提取和检测情感,如自然语言处理(NLP)和机器学习(ML)算法。目前的工作旨在重用和评估一个智能模型来分析Twitter上葡萄牙语文本的积极或消极,考虑到这些感受可能是抑郁的指标。因此,我们将ML算法与SA和NLP技术结合使用,准确率达到79%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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