Sentiment Analysis in Transcribed Utterances

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.

[1]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[2]  Thomas Oommen,et al.  Sampling Bias and Class Imbalance in Maximum-likelihood Logistic Regression , 2011 .

[3]  Youngja Park,et al.  Towards real-time measurement of customer satisfaction using automatically generated call transcripts , 2009, CIKM.

[4]  Koen W. De Bock,et al.  An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction , 2011, Expert Syst. Appl..

[5]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Yuval Elovici,et al.  CoBAn: A context based model for data leakage prevention , 2014, Inf. Sci..

[7]  Gerald Penn,et al.  Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  Katharina Morik,et al.  Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring , 1999, ICML.

[9]  R. Hallowell.  The relationships of customer satisfaction, customer loyalty, and profitability: an empirical study , 1996 .

[10]  Alessandro Vinciarelli.  Noisy Text Categorization , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Lior Rokach,et al.  Methodology for Connecting Nouns to Their Modifying Adjectives , 2014, CICLing.

[12]  R. Rust,et al.  Customer Satisfaction, Productivity, and Profitability: Differences Between Goods and Services , 1997 .

[13]  Shourya Roy,et al.  Automatic Generation of Domain Models for Call-Centers from Noisy Transcriptions , 2006, ACL.

[14]  Chun-Xia Zhang,et al.  RotBoost: A technique for combining Rotation Forest and AdaBoost , 2008, Pattern Recognit. Lett..

[15]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[16]  Joseph Polifroni,et al.  Can prosody inform sentiment analysis? Experiments on short spoken reviews , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Rui Xia,et al.  Exploring the Use of Word Relation Features for Sentiment Classification , 2010, COLING.

[18]  Man Lung Yiu,et al.  Group-by skyline query processing in relational engines , 2009, CIKM.

[19]  Thomas L. Griffiths,et al.  Contextual Dependencies in Unsupervised Word Segmentation , 2006, ACL.

[20]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[21]  Greta E. Greer,et al.  Understanding topics and sentiment in an online cancer survivor community. , 2013, Journal of the National Cancer Institute. Monographs.

[22]  Junlan Feng,et al.  Robust Sentiment Detection on Twitter from Biased and Noisy Data , 2010, COLING.

[23]  W. Bruce Croft,et al.  A Language Modeling Approach to Information Retrieval , 1998, SIGIR Forum.

[24]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[25]  Irwin King,et al.  Let's Tango - Finding the Right Couple for Feature-Opinion Association in Sentiment Analysis , 2009, PAKDD.

[26]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Hiroya Takamura,et al.  Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees , 2005, PAKDD.

[28]  Jun Zhao,et al.  Opinion Target Extraction Using Partially-Supervised Word Alignment Model , 2013, IJCAI.

[29]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[30]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.