TY - GEN
T1 - Constructing sentiment sensitive vectors for word polarity classification
AU - Chu, Chun Han
AU - Roopa, Apoorva Honnegowda
AU - Chang, Yung Chun
AU - Hsu, Wen Lian
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/12
Y1 - 2016/2/12
N2 - Sentiment classification has been an essential part of opinion mining and sentiment analysis. This topic has been applied to real world scenarios such as mining customer reviews on merchandise sold online and film reviews of movies. Therefore, we aimed to gain insight into sentiment word classification, as it could serve as the foundation for larger scale sentiment analyses on corpuses and documents. In this paper, we focus on word polarity classification, which could be extended to perform classification of sentences and paragraphs. We enhanced our previous work on gloss vector and expanded it with a more concise method to generate the vectors. Additionally, we used more sources to validate the similarities of the candidates with two vectors, each representing the positive and negative sentiment polarity respectively by importing groups of words that express that polarity. Experiment results demonstrated that our method is effective, while producing better accuracies than the previous attempt on similar subjects.
AB - Sentiment classification has been an essential part of opinion mining and sentiment analysis. This topic has been applied to real world scenarios such as mining customer reviews on merchandise sold online and film reviews of movies. Therefore, we aimed to gain insight into sentiment word classification, as it could serve as the foundation for larger scale sentiment analyses on corpuses and documents. In this paper, we focus on word polarity classification, which could be extended to perform classification of sentences and paragraphs. We enhanced our previous work on gloss vector and expanded it with a more concise method to generate the vectors. Additionally, we used more sources to validate the similarities of the candidates with two vectors, each representing the positive and negative sentiment polarity respectively by importing groups of words that express that polarity. Experiment results demonstrated that our method is effective, while producing better accuracies than the previous attempt on similar subjects.
KW - lexical taxonomy
KW - sentiment analysis
KW - sentiment sensitive vector
KW - word polarity classification
KW - word similarity
UR - http://www.scopus.com/inward/record.url?scp=84964301354&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964301354&partnerID=8YFLogxK
U2 - 10.1109/TAAI.2015.7407058
DO - 10.1109/TAAI.2015.7407058
M3 - Conference contribution
AN - SCOPUS:84964301354
T3 - TAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence
SP - 252
EP - 259
BT - TAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Conference on Technologies and Applications of Artificial Intelligence, TAAI 2015
Y2 - 20 November 2015 through 22 November 2015
ER -