Big data and automatic detection of topics: social network texts
Artículo de revista
2020
Journal of Physics: Conference Series
This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics. To this end, two resources were used to analyze feelings in order to detect these terms. The proposed system was evaluated with real data sets from the Twitter and Facebook social networks in English and Spanish respectively, demonstrating in both cases the influence of sentimentally oriented terms in the detection of topics in social network texts.
- Artículos científicos [3156]
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Big Data and Automatic Detection of Topics. Social Network Texts.pdf
Título: Big Data and Automatic Detection of Topics. Social Network Texts.pdf
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Descripción: Big Data and Automatic Detection of Topics Social Network Texts.pdf
Título: Big Data and Automatic Detection of Topics Social Network Texts.pdf
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Título: Big Data and Automatic Detection of Topics. Social Network Texts.pdf
Tamaño: 790.8Kb
PDFLEER EN FLIP
Descripción: Big Data and Automatic Detection of Topics Social Network Texts.pdf
Título: Big Data and Automatic Detection of Topics Social Network Texts.pdf
Tamaño: 1.441Mb
PDFLEER EN FLIP
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