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Context-based News Headlines Analysis

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dc.contributor.author Hossain, Syeda Sumbul
dc.contributor.author Arafat, Yeasir
dc.contributor.author Hossain, Md. Ekram
dc.date.accessioned 2022-03-20T04:43:06Z
dc.date.available 2022-03-20T04:43:06Z
dc.date.issued 2021
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7537
dc.description.abstract Online news blogs and websites are becoming influential to any society as they accumulate the world in one place. Aside from that, online news blogs and websites have efficient strategies in grabbing readers’ attention by the headlines, that being so to recognize the sentiment orientation or polarity of the news headlines for avoiding misinterpretation against any fact. In this study, we have examined 3383 news headlines created by five different global newspapers. In the interest of distinguishing the sentiment polarity (or sentiment orientation) of news headlines, we have trained our model by seven machine learning and two deep learning algorithms. Finally, their performance was compared. Among them, Bernoulli naïve Bayes and Convolutional Neural Network (CNN) achieved higher accuracy than other machine learning and deep learning algorithms, respectively. Such a study will help the audience in determining their impression against or for any leader or governance; and will provide assistance to recognize the most indifferent newspaper or news blogs. en_US
dc.language.iso en_US en_US
dc.publisher Vietnam Journal of Computer Science en_US
dc.subject Sentiment analysis en_US
dc.subject Opinion mining en_US
dc.subject Semantic orientation en_US
dc.subject Sentiment polarity detection en_US
dc.subject News headline en_US
dc.subject Text mining en_US
dc.title Context-based News Headlines Analysis en_US
dc.title.alternative a Comparative Study of Machine Learning and Deep Learning Algorithms en_US
dc.type Article en_US


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