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A sentiment analysis approach to increase authorship identification
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C Tutor usage in relation to student achievement and progress: a study of introductory programming courses in Portugal and Serbia
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Urban Evolution of Fafe in the Last Two Centuries
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Henriques, Pedro Rangel. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2019. : OASIcs - OpenAccess Series in Informatics. 8th Symposium on Languages, Applications and Technologies (SLATE 2019), 2019
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Scraping news sites and social networks for prejudice term analysis
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Predicting Performance Problems Through Emotional Analysis (Short Paper)
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Predicting Performance Problems Through Emotional Analysis (Short Paper) ...
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Increasing authorship identification through emotional analysis
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Hate speech classification in social media using emotional analysis
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Creating a social media-based personal emotional lexicon
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Abstract:
One of the major problems when using lexicon in sentiment analysis is that they do not cover all possible words in a text and frequently they miss the more expressive to describe the emotions of the text's author efficiently. This problem occurs because people in non-official, on formal channels, communicate using slangs, neologisms, new patterns based on abbreviations (as "aka", "brb" and "asap") and the different meanings, making challenging to analyse texts using a finite subset of a language. This is a problem because some unknown words can completely change the meaning of a sentence, producing misunderstandings. In this paper we present an approach to expand an emotional lexicon for a specific author, producing a customised lexicon which represents how the author "feels" the words. In our experiments, we got an increase of 35.34% and 107.02% in the dictionary size when compared to the original lexicon using two different authors, and identifying different emotions from the same text according to each author's lexicon, i.e. interpreting the text according to the author's "point of view". ; This work has been supported by COMPETE: POCI-01-0145-FEDER-0070 43 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013.
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Keyword:
Ciências Naturais::Ciências da Computação e da Informação; Eletrónica e Informática; Engenharia e Tecnologia::Engenharia Eletrotécnica; Machine Learning; Natural Processing Language; Science & Technology; Sentiment Analysis
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URL: http://hdl.handle.net/1822/68818
https://doi.org/10.1145/3243082.3264668
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Applying Attribute Grammars to Teach Linguistic Rules
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Henriques, Pedro Rangel. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2017. : OASIcs - OpenAccess Series in Informatics. 6th Symposium on Languages, Applications and Technologies (SLATE 2017), 2017
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