TY - GEN
T1 - Sentence-level sentiment polarity classification using a linguistic approach
AU - Tan, Luke Kien Weng
AU - Na, Jin Cheon
AU - Theng, Yin Leng
AU - Chang, Kuiyu
PY - 2011
Y1 - 2011
N2 - Recent sentiment analysis research has focused on the functional relations of words using typed dependency parsing as this provides a refined analysis on the grammar and semantics of the textual data, which could improve performance. However, typed dependencies only provide the grammatical relationships between individual words while there exist more complex relationships between words that could influence a sentence sentiment polarity. In this paper, we propose a linguistic approach, called Polarity Prediction Model (PPM), that combines typed dependencies and subjective phrase analysis to detect sentence-level sentiment polarity. Our approach also considers the intensity of words and domain terms that could influence the sentiment polarity output. PPM is shown to provide a fine-grained analysis for handling and explaining the complex relationships between words in detecting a sentence sentiment polarity. PPM was found to consistently outperform a baseline model by 5% in terms of overall F1-score, and exceeding 10% in terms of positive F1-score when compared to a Typed-dependency only approach.
AB - Recent sentiment analysis research has focused on the functional relations of words using typed dependency parsing as this provides a refined analysis on the grammar and semantics of the textual data, which could improve performance. However, typed dependencies only provide the grammatical relationships between individual words while there exist more complex relationships between words that could influence a sentence sentiment polarity. In this paper, we propose a linguistic approach, called Polarity Prediction Model (PPM), that combines typed dependencies and subjective phrase analysis to detect sentence-level sentiment polarity. Our approach also considers the intensity of words and domain terms that could influence the sentiment polarity output. PPM is shown to provide a fine-grained analysis for handling and explaining the complex relationships between words in detecting a sentence sentiment polarity. PPM was found to consistently outperform a baseline model by 5% in terms of overall F1-score, and exceeding 10% in terms of positive F1-score when compared to a Typed-dependency only approach.
KW - linguistic approach
KW - polarity classification
KW - Sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=80455140260&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80455140260&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24826-9_13
DO - 10.1007/978-3-642-24826-9_13
M3 - Conference contribution
AN - SCOPUS:80455140260
SN - 9783642248252
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 77
EP - 87
BT - Digital Libraries
T2 - 13th International Conference on Asia-Pacific Digital Libraries, ICADL 2011
Y2 - 24 October 2011 through 27 October 2011
ER -