Abstract
The paper investigates a problem connected with automatic analysis of sentiment (opinion) in textual natural-language documents. The initial situation works on the assumption that a user has many documents centered around a certain topic with different opinions of it. The user wants to pick out only relevant documents that represent a certain sentiment – for example, only positive reviews of a certain subject. Having not too many typical patterns of the desired document type, the user needs a tool that can collect documents which are similar to the patterns. The suggested procedure is based on computing the similarity degree between patterns and unlabeled documents, which are then ranked according to their similarity to the patterns. The similarity is calculated as a distance between patterns and unlabeled items. The results are shown for publicly accessible downloaded real-world data in two languages, English and Czech.
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Žižka, J., Dařena, F. (2010). Automatic Sentiment Analysis Using the Textual Pattern Content Similarity in Natural Language. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2010. Lecture Notes in Computer Science(), vol 6231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15760-8_29
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DOI: https://doi.org/10.1007/978-3-642-15760-8_29
Publisher Name: Springer, Berlin, Heidelberg
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