information Article Text Mining and Sentiment Analysis of Newspaper Headlines Arafat Hossain 1 , Md. Karimuzzaman 1 , Md. Moyazzem Hossain 1, * and Azizur Rahman 2, *   Citation: Hossain, A.; Karimuzzaman, M.; Hossain, M.M.; Rahman, A. Text Mining and Sentiment Analysis of Newspaper Headlines. Information 2021, 12, 414. https://doi.org/10.3390/info12100414 Academic Editor: Byung-Won On Received: 1 April 2021 Accepted: 12 August 2021 Published: 9 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Statistics, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh; arafathossen239@gmail.com (A.H.); karimuzzaman.statju@gmail.com (M.K.) 2 School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2678, Australia * Correspondence: hossainmm@juniv.edu (M.M.H.); azrahman@csu.edu.au (A.R.) Abstract: 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. Keywords: newspaper; headlines pattern and context; word cloud; cluster analysis; sentiment analysis; Bangladesh 1. Introduction Text mining is a technique for extracting information from text by recognizing patterns and trends. The term text mining, text analytics, or text analysis refers to the process of retrieving information through lexical resources, tagging or annotation, and techniques such as association, visualization, and prediction. After successfully developing basic natural language processing (NLP) in the 1960s, different adoptions of techniques such as dimension reduction, latent factor identification, and database text processing have contributed to the flourishing of the new era of information retrieval. Moreover, the topic model or latent semantic analysis and machine learning algorithms seemingly gave a more substantial base after the 1990s—sentiment analysis and opinion mining methods have emerged from analysing the sentiment of humans from text, which enthrals intellectual fields including computer science, statistics, linguistics, and social science. Additionally, a successful implication for the analysis of journals, social network services, and online customer reviews, along with email filtering, product suggestions, fraud detection, search engines, and bankruptcy predictions, has increased its significance in all aspects [1–7]. Information 2021, 12, 414. https://doi.org/10.3390/info12100414 https://www.mdpi.com/journal/information