Text Mining and Sentiment Analysis of Newspaper Headlines
Text Mining and Sentiment Analysis of Newspaper Headlines
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DOI：
10.3390/info12100414
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
A. Hossain;Md. Karimuzzaman;Moyazzem Hossain;Azizur Rahman
中科院分区：
文献类型：
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作者：
A. Hossain;Md. Karimuzzaman;Moyazzem Hossain;Azizur Rahman
文本分析在现代以从文本中提取信息和模式而闻名。然而，没有一项研究试图利用文本分析技术的组合来说明孟加拉国报纸标题的模式和优先级。本文的目的是研究2018年和2019年孟加拉国著名英语日报《每日星报》头版上出现的单词模式。对那个时代可能存在的社会和政治语境的阐释，也试图用语态来进行。该研究采用了三种广泛使用的当代文本挖掘技术：词云、情感分析和聚类分析。词汇云显示，选举、杀戮、板球、罗兴亚相关词汇在2018年出现了60多次，而BNP、poll、杀戮、AL、Khaleda等词汇在2019年出现了80多次。这表明该国对板球的热情、政治动荡以及与罗兴亚人有关的问题。此外，情绪分析显示，恐惧和消极情绪的词汇出现了600多次，而愤怒、期待、悲伤、信任和积极情绪的词汇出现了400多次。最后，聚类方法表明，选举、政治、死亡、数字安全法、罗兴亚和板球相关的词在2019年表现出相似性，属于一个相似的组，而强奸、死亡、道路和火灾相关的词在2018年聚在一个相似的组中。总的来说，这一分析显示了文本挖掘方法如何生动地描绘了孟加拉国的社会、政治和法律与秩序状况，特别是在选举季节和该国的板球热潮期间，也验证了文本挖掘方法在以有效方式了解特定时期一个国家的整体观点方面的重要性。
Text analytics are well-known in the modern era for extracting information and patterns from text. However, no study has attempted to illustrate the pattern and priorities of newspaper headlines in Bangladesh using a combination of text analytics techniques. The purpose of this paper is to examine the pattern of words that appeared on the front page of a well-known daily English newspaper in Bangladesh, The Daily Star, in 2018 and 2019. The elucidation of that era’s possible social and political context was also attempted using word patterns. The study employs three widely used and contemporary text mining techniques: word clouds, sentiment analysis, and cluster analysis. The word cloud reveals that election, kill, cricket, and Rohingya-related terms appeared more than 60 times in 2018, whereas BNP, poll, kill, AL, and Khaleda appeared more than 80 times in 2019. These indicated the country’s passion for cricket, political turmoil, and Rohingya-related issues. Furthermore, sentiment analysis reveals that words of fear and negative emotions appeared more than 600 times, whereas anger, anticipation, sadness, trust, and positive-type emotions came up more than 400 times in both years. Finally, the clustering method demonstrates that election, politics, deaths, digital security act, Rohingya, and cricket-related words exhibit similarity and belong to a similar group in 2019, whereas rape, deaths, road, and fire-related words clustered in 2018 alongside a similar-appearing group. In general, this analysis demonstrates how vividly the text mining approach depicts Bangladesh’s social, political, and law-and-order situation, particularly during election season and the country’s cricket craze, and also validates the significance of the text mining approach to understanding the overall view of a country during a particular time in an efficient manner.