A Japanese Corpus for Analyzing Customer Loyalty Information

A Japanese Corpus for Analyzing Customer Loyalty Information Yiou Wang, Tahara Takuji Fuji Xerox Co., Ltd 6-1, Minatomirai, Nishi-ku, Yokohama-shi, Kanagawa 220-8668, Japan {Yiou.Wang, Tahara.Takuji} @fujixerox.co.jp Abstract 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. Keywords: Customer loyalty, Voice of customer, Opinion mining 1. Introduction Customers voice attitudes, opinions and their experience about some brands, companies, products or services. To- day voice of the customer (VOC), capturing a view of cus- tomer’s behaviors, needs, and feedbacks, can be obtained through center calls, emails, questionnaire, web reviews or SNS (Subramaniam et al., 2012; Aguwa et al., 2012; Choi et al., 2013; Saeed et al., 2013). Such unstructured tex- tual information contains valuable information for market- ing analysis. On the other hand, customer loyalty has long been a topic of high interest in both academia and indus- try (Griffin, 2002; Asuncion et al., 2004; Scriosteanu and Popescu, 2010), and is critical for business profitability. Therefore, it is attractive to consider exploiting customer loyalty by analyzing the voice of customer. To automatically find or track the attitudes, feelings and evaluations in texts, opinion mining and sentiment analy- sis have been extensively studied from different perspec- tives (Kushal et al., 2003; Pang and Lee, 2008; Nakagawa et al., 2008; Wang et al., 2012). There are also several corpus for opinion mining, such as MPQA Opinion Cor- pus version 3.0 (Deng and Wiebe, 2015), JDPA Corpus (Kessler et al., 2010) and Chinese evaluation information corpus (Wang et al., 2012). All the existing works are related to the affective and evaluative dimensions and fo- cus on understanding customer satisfaction. However, cus- tomer loyalty is not customer satisfaction. Satisfaction is a necessary but not sufficient criterion for customer loy- alty (Griffin, 2002; Ganiyu et al., 2012). We know that ”very satisfied” to ”satisfied” customers sometimes switch to competitors. …

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