Analysis of the Textual Information Extracted from News Portals

Lovric, Petra; Vickovic, L.; Karna, Hrvoje

doi:10.23919/softcom55329.2022.9911444

Cited by 1 publication

(3 citation statements)

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“…The overall result for all articles is that emotions range from -25 to 25. From the results of the sentiment analysis it is obvious that the values acquired in this study are much more extreme in comparison to the results from our previous study [20]. This is somewhat expected, as headlines tend to use a lot more emotionally charged words to grab the reader's attention.…”

Section: Lda Algorithmcontrasting

confidence: 44%

“…This paper builds on top of our previous work [20], but it considers the problem from another perspective. The presented study intends to determine the characteristics of the information that readers were exposed to through the most prominent and impressionable part of the posts [21], their headlines.…”

Section: Introductionmentioning

confidence: 99%

“…This means that after the content identification, it was exported into a data file format (.xls) suitable for import into a data mining environment, and it was structured in the form shown in TableIII. A comparison of our earlier work (SoftCOM'22 paper[20]) corpus features with those used in this study are shown, as follows:…”

mentioning

confidence: 99%

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Data Analysis of the Web News Headlines based on Natural Language Processing

Karna

Braović

Vickovic

et al. 2023

JCOMSS

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This paper explores the problem of media content data analysis with the focus on the phenomenon of vaccination, closely related to the COVID-19 pandemic. The presented research is an extension of the previous work, but it differs in two main areas. Firstly, the text corpus submitted to the analysis has been considerably increased. Secondly, the previous data analysis was performed on the body part of the posts, while now it is focused on the most prominent part of the news posts, their headlines. This change from body to headline analysis was provoked by significant differences in their characteristics and the fact that most people read only headlines. Described data acquisition uses an advanced content collection approach followed by the modeling process, during which a set of natural language processing algorithms were applied. To enable the comparison, the model uses the same set of algorithms in the modeling phase like in previous work. The main contributions of the work are manifested in: i) approaching the problem from a new perspective, ii) applying more efficient method of data collection, and crucially iii) enabling the comparison of analysis results for individual parts of the content, which ensured a comprehensive insight into the characteristics of news posts.

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Section: Lda Algorithmcontrasting

confidence: 44%

Section: Introductionmentioning

confidence: 99%