Building and evaluating resources for sentiment analysis in the Greek language.

Sentiment lexicons and word embeddings constitute well-established sources of information for sentiment analysis in online social media. Although their effectiveness has been demonstrated in state-of-the-art sentiment analysis and related tasks in the English language, such publicly available resour...

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Publicado en:Language Resources & Evaluation Vol. 52; no. 4; pp. 1021 - 1045
Autores principales: Tsakalidis, Adam, Papadopoulos, Symeon, Voskaki, Rania, Ioannidou, Kyriaki, Boididou, Christina, Cristea, Alexandra I., Liakata, Maria, Kompatsiaris, Yiannis
Formato: Artículo
Publicado: Springer Nature Dec2018
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Acceso en línea:Ver este registro en EBSCOhost
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        atl: Building and evaluating resources for sentiment analysis in the Greek language.
      aug:
        au:
          Tsakalidis, Adam
          Papadopoulos, Symeon
          Voskaki, Rania
          Ioannidou, Kyriaki
          Boididou, Christina
          Cristea, Alexandra I.
          Liakata, Maria
          Kompatsiaris, Yiannis
        affil:
          Department of Computer Science, University of Warwick, Coventry, UK
          The Alan Turing Institute, London, UK
          Information Technologies Institute, CERTH, Thessaloníki, Greece
          Centre for the Greek Language, Thessaloníki, Greece
          Laboratory of Translation and Natural Language Processing, Aristotle University of Thessaloniki, Thessaloníki, Greece
          Department of Computer Science, University of Durham, Durham, UK
      su:
        Sentiment analysis
        Greek language
        Computational linguistics
        English language
        Lexicon
      sug:
        subj:
          Sentiment analysis
          Greek language
          Computational linguistics
          English language
          Lexicon
      keyword:
        Emotion analysis
        Natural language processing
        Opinion mining
        Sarcasm detection
        Sentiment lexicon
        Word embeddings
      ab: Sentiment lexicons and word embeddings constitute well-established sources of information for sentiment analysis in online social media. Although their effectiveness has been demonstrated in state-of-the-art sentiment analysis and related tasks in the English language, such publicly available resources are much less developed and evaluated for the Greek language. In this paper, we tackle the problems arising when analyzing text in such an under-resourced language. We present and make publicly available a rich set of such resources, ranging from a manually annotated lexicon, to semi-supervised word embedding vectors and annotated datasets for different tasks. Our experiments using different algorithms and parameters on our resources show promising results over standard baselines; on average, we achieve a 24.9% relative improvement in F-score on the cross-domain sentiment analysis task when training the same algorithms with our resources, compared to training them on more traditional feature sources, such as n-grams. Importantly, while our resources were built with the primary focus on the cross-domain sentiment analysis task, they also show promising results in related tasks, such as emotion analysis and sarcasm detection.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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      custom: Language Resources & Evaluation is a copyright of Springer, 2018. All Rights Reserved.
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