A Methodological Framework for AI-Driven Textual Data Analysis in Digital Media
The growing volume of textual data generated on digital media platforms presents significant challenges for the analysis and interpretation of information. This article proposes a methodological approach that combines artificial intelligence (AI) techniques and statistical methods to explore and ana...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/2/59 |
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| author | Douglas Cordeiro Carlos Lopezosa Javier Guallar |
| author_facet | Douglas Cordeiro Carlos Lopezosa Javier Guallar |
| author_sort | Douglas Cordeiro |
| collection | DOAJ |
| description | The growing volume of textual data generated on digital media platforms presents significant challenges for the analysis and interpretation of information. This article proposes a methodological approach that combines artificial intelligence (AI) techniques and statistical methods to explore and analyze textual data from digital media. The framework, titled DAFIM (Data Analysis Framework for Information and Media), includes strategies for data collection through APIs and web scraping, textual data processing, and data enrichment using AI solutions, including named entity recognition (people, locations, objects, and brands) and the detection of clickbait in news. Sentiment analysis and text clustering techniques are integrated to support content analysis. The potential applications of this methodology include social networks, news aggregators, news portals, and newsletters, offering a robust framework for studying digital data and supporting informed decision-making. The proposed framework is validated through a case study involving data extracted from the Google News aggregation platform, focusing on the Israel–Lebanon conflict. This demonstrates the framework’s capability to uncover narrative patterns, content trends, and clickbait detection while also highlighting its advantages and limitations. |
| format | Article |
| id | doaj-art-cff425df29fc4f32a0627329ea793e3e |
| institution | DOAJ |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-cff425df29fc4f32a0627329ea793e3e2025-08-20T03:12:11ZengMDPI AGFuture Internet1999-59032025-02-011725910.3390/fi17020059A Methodological Framework for AI-Driven Textual Data Analysis in Digital MediaDouglas Cordeiro0Carlos Lopezosa1Javier Guallar2Faculty of Information and Communication, Federal University of Goiás, Goiânia 74690-900, GO, BrazilFaculty of Information and Audiovisual Media, University of Barcelona, 08193 Barcelona, SpainFaculty of Information and Audiovisual Media, University of Barcelona, 08193 Barcelona, SpainThe growing volume of textual data generated on digital media platforms presents significant challenges for the analysis and interpretation of information. This article proposes a methodological approach that combines artificial intelligence (AI) techniques and statistical methods to explore and analyze textual data from digital media. The framework, titled DAFIM (Data Analysis Framework for Information and Media), includes strategies for data collection through APIs and web scraping, textual data processing, and data enrichment using AI solutions, including named entity recognition (people, locations, objects, and brands) and the detection of clickbait in news. Sentiment analysis and text clustering techniques are integrated to support content analysis. The potential applications of this methodology include social networks, news aggregators, news portals, and newsletters, offering a robust framework for studying digital data and supporting informed decision-making. The proposed framework is validated through a case study involving data extracted from the Google News aggregation platform, focusing on the Israel–Lebanon conflict. This demonstrates the framework’s capability to uncover narrative patterns, content trends, and clickbait detection while also highlighting its advantages and limitations.https://www.mdpi.com/1999-5903/17/2/59digital medianatural language processing (NLP)text analysissentiment analysisartificial intelligencestatistics |
| spellingShingle | Douglas Cordeiro Carlos Lopezosa Javier Guallar A Methodological Framework for AI-Driven Textual Data Analysis in Digital Media Future Internet digital media natural language processing (NLP) text analysis sentiment analysis artificial intelligence statistics |
| title | A Methodological Framework for AI-Driven Textual Data Analysis in Digital Media |
| title_full | A Methodological Framework for AI-Driven Textual Data Analysis in Digital Media |
| title_fullStr | A Methodological Framework for AI-Driven Textual Data Analysis in Digital Media |
| title_full_unstemmed | A Methodological Framework for AI-Driven Textual Data Analysis in Digital Media |
| title_short | A Methodological Framework for AI-Driven Textual Data Analysis in Digital Media |
| title_sort | methodological framework for ai driven textual data analysis in digital media |
| topic | digital media natural language processing (NLP) text analysis sentiment analysis artificial intelligence statistics |
| url | https://www.mdpi.com/1999-5903/17/2/59 |
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