SF-CNN: Deep Text Classification and Retrieval for Text Documents

Sarasu, R.; Thyagharajan, K. K.; Shanker, N. R.

doi:10.32604/iasc.2023.027429

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…In order to correlate the data and create more accurate thematic categories, we also used the TextRazor interface, a commercial tool that scored best in terms of accuracy in textual analysis compared to other similar resources [21]. This application uses a set of machine learning algorithms and, through statistical topic classification, extracts what it calls "entities", the main topics present in the text [22].…”

Section: Content Analysismentioning

confidence: 99%

Content and Sentiment Analysis of Autobiographical Narratives of Experienced and Well-Evaluated Teachers in Spain

Barbosa,

Marín-Suelves

2024

Education Sciences

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Analysing the content and feelings of teachers’ autobiographical narratives provides relevant information about teaching processes, challenges in professional development, performance, and identity. In this work, we analysed the narratives of five well-evaluated Spanish teachers with more than 10 years of experience. We used Iramuteq, RStudio, and TextRazor to describe the main themes, analyse the emotions, and check whether evidence-based teaching practices were mentioned. The results showed that emotional aspects were central, with positive feelings dominating. In the content analysis, themes such as mental health, inclusion, immigration, war, empathy, critical pedagogy, and autonomous and emancipatory education stand out. The analyses are relevant for teacher educators, policy makers, researchers, and teachers in training or in active service.

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Section: Content Analysismentioning

confidence: 99%