Today customers voice attitudes, opinions and their experience about some brands, companies, products or services through center calls, web reviews or SNS and analyzing them is an important task. On the other hand, customer loyalty has long been a topic of high interest in both academia and industry. Therefore, it is attractive to consider exploiting customer loyalty information by analyzing the voice of customer. However, although many previous studies focused on analyzing attitudes, opinions, sentiments of the text data, no work has been conducted from the perspective of customer loyalty, which is reflected by a combination of customer attitudes and behavior. In this work, we present JCLIC, Japanese Customer Loyalty Information Corpus, which is a corpus for analyzing customer loyalty information. For each review we have annotated detailed customer loyalty information which contains: loyalty degree that reflects loyalty level of the customer, loyalty expression that expresses the customer loyalty, loyalty type that indicates the category to which loyalty expression belongs., reason expression that expresses why the customer have such loyalty degree, and reason type that indicates the category to which reason expression belongs.. We describe our annotation scheme and annotation process, present results of an agreement study and give some statistics about the corpus we have annotated.
@InProceedings{WANG18.465, author = {Yiou Wang and Takuji Tahara}, title = "{A Japanese Corpus for Analyzing Customer Loyalty Information}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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