TY - GEN
T1 - Sentiment analysis in transcribed utterances
AU - Ofek, Nir
AU - Katz, Gilad
AU - Shapira, Bracha
AU - Bar-Zev, Yedidya
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - A single phone call can make or break a valuable customer-organization relationship. Maintaining good quality of service can lead to customer loyalty, which affects profitability. Traditionally, customer feedback is mainly collected by interviews, questionnaires, and surveys; the major drawback of these data collection methods is in their limited scale. The growing amount of research conducted in the field of sentiment analysis, combined with advances in text processing and Artificial Intelligence, has led us be the first to present an intelligent system for mining sentiment from transcribed utterances—wherein the noisiness property and short length poses extra challenges to sentiment analysis. Our aim is to detect and process affective factors from multiple layers of information, and study the effectiveness and robustness of each factor type independently, by proposing a tailored machine learning paradigm. Three types of factors are related to the textual content while two overlook it. Experiments are carried out on two datasets of transcribed phone conversations, obtained from real-world telecommunication companies.
AB - A single phone call can make or break a valuable customer-organization relationship. Maintaining good quality of service can lead to customer loyalty, which affects profitability. Traditionally, customer feedback is mainly collected by interviews, questionnaires, and surveys; the major drawback of these data collection methods is in their limited scale. The growing amount of research conducted in the field of sentiment analysis, combined with advances in text processing and Artificial Intelligence, has led us be the first to present an intelligent system for mining sentiment from transcribed utterances—wherein the noisiness property and short length poses extra challenges to sentiment analysis. Our aim is to detect and process affective factors from multiple layers of information, and study the effectiveness and robustness of each factor type independently, by proposing a tailored machine learning paradigm. Three types of factors are related to the textual content while two overlook it. Experiments are carried out on two datasets of transcribed phone conversations, obtained from real-world telecommunication companies.
KW - Customer satisfaction
KW - Noisy text mining
KW - Sentiment analysis
UR - https://www.scopus.com/pages/publications/84945567247
U2 - 10.1007/978-3-319-18032-8_3
DO - 10.1007/978-3-319-18032-8_3
M3 - Conference contribution
AN - SCOPUS:84945567247
SN - 9783319180311
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 27
EP - 38
BT - Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
A2 - Cao, Tru
A2 - Lim, Ee-Peng
A2 - Ho, Tu-Bao
A2 - Zhou, Zhi-Hua
A2 - Motoda, Hiroshi
A2 - Cheung, David
PB - Springer Verlag
T2 - 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
Y2 - 19 May 2015 through 22 May 2015
ER -