Authors:
Shota Suzuki
;
Ryohei Orihara
;
Yuichi Sei
;
Yasuyuki Tahara
and
Akihiko Ohsuga
Affiliation:
University of Electro-Communications, Japan
Keyword(s):
Natural Languages Processing, Opinion Mining, Sarcasm, Sentiment, Online Review.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Applications
;
Artificial Intelligence
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Symbolic Systems
;
Web Information Systems and Technologies
;
Web Intelligence
Abstract:
Currently, classifying sarcastic sentences into positive and negative sentiments has been a difficult problem
and an important task. The sarcastic sentences could indicate negative meaning by using positive expressions,
or positive meaning by using negative expressions. Sarcasm is a special kind of sentiment that comprise of
words which mean the opposite of what you really want to say, especially in order to insult or wit someone, to
show irritation, or to be funny. Therefore, determining sarcasm is an important task in order to correctly classify
the sentence. In this paper, we propose an approach to detect sarcasm. First, we apply dependency parsing to
amazon review data. After that, we classify phrases in the sentence into the proposed phrase based on the
sequence of part-of-speech as proposed by Bharti et al. After being classified into either one of the phrase
types, it is determined whether each phrase is positive or negative. If the emotions of the situation phrases an
d
the sentiment phrases are different, the sentence is determined to be a “sarcasm”. Using the above method,
the experimental result shows the effectiveness of our method as compared with the the existing research.
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