Five-Dimensional Sentiment Analysis of Corpora, Documents and Words
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A4 Artikkeli konferenssijulkaisussa
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Date
2014
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en
Pages
10
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Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, WSOM 2014, pp. 209-218, Advances in Intelligent Systems and Computing ; Volume 295
Abstract
Sentiment analysis has become a widely used approach to assess the emotional content of written documents such as customer feedback. In positive psychology research, the typical one-dimensional analysis framework has been extended to include five dimensions. This five-dimensional model, PERMA, enables a fine-grained analysis of written texts. We propose an approach in which this model, statistical analysis and the self-organizing map are used. We analyze corpora from various genres. A hybrid methodology that uses the self-organizing maps algorithm and human judgment is suggested for expanding the PERMA lexicon. This vocabulary expansion can be useful for English but it is potentially even more crucial in the case of other languages for which the lexicon is not readily available. The challenges and solutions related to the text mining of texts written in a morphologically complex language such as Finnish are also considered.Description
Keywords
education, independent component analysis, life-philosophical lecturing, natural language processing, positive psychology, self-organizing map, Text mining
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Citation
Honkela, T, Korhonen, J, Lagus, K & Saarinen, E 2014, Five-Dimensional Sentiment Analysis of Corpora, Documents and Words . in Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, WSOM 2014 . Advances in Intelligent Systems and Computing, vol. 295, Springer, pp. 209-218, Workshop on Self-Organizing Maps, Mittweida, Germany, 02/07/2014 . https://doi.org/10.1007/978-3-319-07695-9_20