分析新冠肺炎推文的情绪变化检测。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Panagiotis C Theocharopoulos, Anastasia Tsoukala, Spiros V Georgakopoulos, Sotiris K Tasoulis, Vassilis P Plagianakos
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引用次数: 1

摘要

新冠肺炎大流行对社会产生了重大影响,包括广泛实施封锁以防止病毒传播。这项措施减少了面对面的社交互动,也增加了推特等社交媒体平台的使用。作为工业4.0的一部分,情绪分析可以用来研究公众对未来流行病和一般社会政治局势的态度。这项工作通过结合自然语言处理技术和机器学习算法,将每条推文的情绪分为积极或消极,提出了一个分析框架。通过广泛的实验,我们揭示了这项任务的理想模型,并随后利用情绪预测在疫情期间进行时间序列分析。此外,还应用了一种变化点检测算法,以确定公众对疫情态度的转折点,并通过交叉引用该特定时期的新闻报道进行了验证。最后,我们研究了社交媒体上的情绪趋势与疫情新闻报道之间的关系,深入了解公众对疫情的看法及其对新闻的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analysing sentiment change detection of Covid-19 tweets.

Analysing sentiment change detection of Covid-19 tweets.

Analysing sentiment change detection of Covid-19 tweets.

Analysing sentiment change detection of Covid-19 tweets.

The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public's perception of the pandemic and its influence on the news.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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