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2021 Vol.36, Issue 4 Preview Page

Research Article

February 2021. pp. 525-552
This study introduces the implementation of the DecoFESA platform, which proposes a hybrid approach to FbSA (Feature-based Sentiment Analysis) using the DECO-LGGs (Local Grammar Graphs based on DECO dictionary) assigned with dependency parsing and LSTM (Long Short-Term Memory). DecoFESA is developed to leverage DECO-LGG linguistic resources and NLP_HUB dependency parser to process semantic information and analyze sentence structures for Target, Feature, and Sentiment detection at sentence level. It also uses of one of the efficient deep learning algorithms, LSTM, to classify sentiments of OOV (Out of Vocabulary) texts. For the performance evaluation, we scraped 1,500,000 cosmetics reviews of online shopping malls by a crawler. DecoFESA shows the overall robust performance: 0.95 and 0.80 f-measure for POSITIVE and NEGATIVE with 0.845 and 0.83 accuracies for Target and Feature extraction, which manifests the efficiency and practicality of the hybrid approach to FbSA utilizing extensive DECO-LGG linguistic resources collaborated with dependency parsing and LSTM.
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  • Publisher :The Modern Linguistic Society of Korea
  • Publisher(Ko) :한국현대언어학회
  • Journal Title :The Journal of Studies in Language
  • Journal Title(Ko) :언어연구
  • Volume : 36
  • No :4
  • Pages :525-552