State-of-the-art review on Twitter Sentiment Analysis

N. Alshammari, Amal Almansour
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引用次数: 9

Abstract

In the last few years, Twitter becomes the most popular platform for individuals to share their experiences and viewpoints towards different products and services. Therefore, it attracts a lot of researchers to use it as a body for sentiment analysis and opinion mining research studies. Most of the previous research studies in this area have been using the traditional machine learning-based and lexicon-based approaches more compared to the deep learning approach to classify the emotional states of English tweets. Also, there is a shortage of research studies that categorize the opinion orientations of tweets in other languages such as Arabic. Recently, deep learning approach has achieved remarkable results over the traditional machine learning algorithms in analyzing a massive amount of data as the case with social networks data. In this research study, we seek to discuss the state-of-the-art of sentiment analysis methodologies used to classify tweets' sentiment orientation and challenges that need to be addressed. Also, this paper provides an overview of deep learning approach and question if this approach can be adopted to improve the classification accuracy of sentiment analysis for both English and Arabic tweets.
最新的Twitter情感分析评论
在过去的几年里,Twitter成为个人分享他们对不同产品和服务的经验和观点的最受欢迎的平台。因此,它吸引了许多研究者将其作为情感分析和意见挖掘研究的主体。与深度学习方法相比,该领域之前的研究大多是使用传统的基于机器学习和基于词典的方法来对英语推文的情绪状态进行分类。此外,对阿拉伯语等其他语言的推文的观点取向进行分类的研究也很缺乏。近年来,深度学习方法在分析海量数据方面取得了比传统机器学习算法显著的成果,如社交网络数据。在这项研究中,我们试图讨论最先进的情感分析方法,用于分类推文的情感取向和需要解决的挑战。此外,本文概述了深度学习方法,并提出了是否可以采用这种方法来提高英语和阿拉伯语推文情感分析的分类精度的问题。
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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