A Hybrid Approach to Sentiment Analysis Enhanced by Sentiment Lexicons and Polarity Shifting Devices

Gwanghoon Yoo (1) , Jeesun Nam (1)

1 DICORA - Digital Language & Knowledge Contents Research Association

This paper presents a hybrid approach to sentiment classification method for Korean texts. It is based on a cascading system by which lexicon-based classification first conducts the sentiment detection along with the local parsing of sentiment constituents, and a supervised machine learning algorithm processes the texts for which lexicon-based annotation was unsuccessful. We use a fine-grained Korean machine-readable dictionary for the lexicon-based classification, dealing with Polarity Shifting Devices (PSDs) which are divided into Intensifier, Switcher, Activator, and Nullifier. By structuring PSDs and polarity values of opinion texts, it is possible to process complex sentiment constituents efficiently, including structures resulting from double negation. Through the performance evaluation, we prove that this hybrid approach with sentiment lexicons and PSDs outperforms the baselines.

Mots clés: Sentiment Analysis; Sentiment Lexicon; Polarity-Shifting Device; Hybrid Approach; NLP dictionary; Local Grammar; Local Grammar Graph; LGG; Unitex

Domaines: Traitement du texte et du document; Informatique et langage [cs.CL]

Gwanghoon Yoo, Jeesun Nam. A Hybrid Approach to Sentiment Analysis Enhanced by Sentiment Lexicons and Polarity Shifting Devices. The 13th Workshop on Asian Language Resources, Kiyoaki Shirai, May 2018, Miyazaki, Japan. pp.21-28. ⟨hal-01795217⟩