An Affect-Based Text Mining System for Qualitative Analysis of Japanese Free Text

Manufacturing companies are concerned about ordinary customers’ opinions of their products. This paper presents an affect-based text mining system for Japanese that can aid in the analysis of customers’ reviews of commercial products. From free-text product reviews, the system creates an Adjective-Relation Database containing relations extracted between adjective and noun phrases. Using a pre-existing lexicon of general affect words together with scaled Positive/Negative evaluation, the system provides a quantified evaluation of modified-noun phrases appearing in the product survey. In addition, using correspondence analysis techniques, an Affect Map reflecting the preferences of target customers is created displaying the relations between user profile data and positive and negative affect-laden words. In a practical experiment, we used the results of such correspondence analysis between high-frequency adjective phrases and customer profiles on a questionnaire-based survey of opinions on a brand-name handbag, to further modify our affect lexicon, improving correspondence results. Introduction: The Commercial Challenge In more and more Internet contexts, ordinary customers are expressing their opinions about commercial products (e.g., on consumer-related bulletin boards or in newsgroups). Increasingly, companies are monitoring “the voice of the customer” directly via questionnaires at their web sites. Such product reviews by customers play valuable roles for companies, from providing frank information about preferences to revealing attitudes about competitors. In cases where the functional needs of the customer might be satisfied by several alternative products, it is often the emotional response of the customer to the product that makes the critical difference. Thus, in the hope of understanding such attitudes, many users of our business systems are especially interested in a qualitative analysis of customers’ free-text comments on products. More specifically, users want to know which elements of their products have emotion-invoking effects and, subsequently, how perceived emotional benefits may affect their customers’ buying decisions. This paper describes a text mining system for the qualitative analysis of Japanese free text that attempts to address such issues. Past work on the problem of qualitative analysis of text reflects a variety of approaches. Some researchers in Japan have developed methods to handle qualitative information as image search systems (Watanabe, Nakamura and Nagao 1993; Kiyoki, Kaneko, and Kitagawa 1996; Harada, Itoh, and Nakatani 1999; Mukunoki, Tanaka, and Ikeda 2001). Watanabe et al. (1993) present a search system for painting works with key subjective features extracted from the attached texts. They used the semantic features of all nouns and verbs found in a thesaurus. Harada et al. (1999) studied searching for pictures of chairs with free text queries including some adjectives. They represented subjective features in a parameter space specified for each commercial product, for example, chairs and televisions. They used the semantic differential method for constructing the feature parameter space. However, such methods are not practical for commercial systems because of the prohibitive cost of applying features to all the words used to describe commercial products. A great deal of effort would also be required if users wanted to modify features. In previous work on subjectivity learning, Hatzivassiloglou and McKeown (1997) presented a method for automatically assigning semantic orientation labels to adjectives. Hatzivassiloglou and Wiebe (2000) described a method for classifying adjectives as gradable or nongradable. Wiebe (2000) applied the method to subjectivity tagging. Many of these learning methods require large corpora. However, it is difficult and impractical for users to prepare large corpora for each survey if the expected number of resultant reviews is too small to be used for learning. Pang, Lee and Vaithyanathan (2002) and Turney (2002) have developed methods for classifying reviews. Their approach focused on document-based classification. However, many questionnaires include both “fixedresponse” questions and free-text responses or comments on individual topics, designed to capture more detail about customers' opinions (e.g., positive or negative). So it is not always appropriate to classify whether a review (itself) is positive or negative at the document level. In previous work on affect-based text mining, Subasic and Huettner (2000) used fast analysis and visualization of affect content for decision-making. This work is perhaps the closest to ours. But they focused on document-based affect analysis, not individual survey items or product facets. In contrast, Tateishi, Ishiguro and Fukushima (2001) in their work on opinion information retrieval and Morinaga, Yamanishi, Tateishi and Fukushima (2002) in their work on mining product reputations focused on affect analysis of the products themselves. Our users want an affect-based text mining system focused on the elements of their products, not on the products themselves for the purpose of making decisions about new product development or marketing strategies. Thus, to develop a practical commercial system, we focused on partial sentences that include typical adjectival expressions of customers' qualitative comments and focused on phrase-relation-based affect analysis. The principal goals of our affect-based text mining system are the following: 1. Minimize user’s initial cost to prepare lexical resources; 2. Optimize functions for questionnaire analysis; and 3. Make it easy and efficient to modify lexical resources to accommodate various users’ products and customers' preferences. Creating an Affect Processing System Definition of Phrases The key features of our phrase-relation-based affect analysis system are adjective phrases and noun phrases. Definition of Adjective Phrase. Okada (1985) defines the function of adjectives (in Japanese) as representing the concept of difference. We adopt this definition and use the following template to distinguish adjective phrases from other phrases, independently of the syntactic categories of the morphemes that are actually used in the phrase. ○○についていえば,A は B より×× Evaluating ○○, A is more ×× (much ××-er) than B. The phrase that appears as ×× represents an evaluation of the qualitative difference between A and B. We define such a phrase as an adjective phrase. For example, in the sentence: “接客について言えば、A さんは B さんより「親切」だ (“As for receptionists, I’d say A-san is kinder than B-san.)”, we can treat kind as an adjective phrase. Note again that in Japanese, as long as a phrase denotes a qualitative difference (between two things), it is considered to be an adjective phrase, even if the phrase does not contain an adjective morpheme (e.g., 品がない (lacking in refinement); ウキウキする (cheerfully feel); 使いづらい (hard to use)). Definition of Modified-Noun Phrase. Okada (1985) observes that nouns, sometimes explicitly, sometimes tacitly, have contextual relations to adjectives. These relations are very important in expressing the qualitative features of objects. We address this in our work by specifically defining the noun phrase in relation to adjective phrase as a modified-noun phrase. Adjective-Relation Database Structure of Adjective-Relation Database. The Adjective-Relation Database has relational information about adjective phrases extracted from the free-text reviews. The database contains adjective phrases, modified-noun phrases in relation to the adjective phrases, the free texts as sources of extraction, and customers' individual profiles (if such exist). The individual profiles are used for modifying the affect lexicon. Table 1 presents the database structure and some example entries from the database. By using the database, users can extract adjective phrase relation patterns, parts of free text, and the customers' profiles. Creating Adjective-Relation Database. The AdjectiveRelation Database is built from the surface syntactic parser (Zhai, Tong, Milic-Frayling, and Evans 1997) used in the ConceptBase information management system (Fujita 1999), extracting adjective phrases and noun phrases from text after morphological processing. The database builder normalizes the surface forms of the adjective phrases and noun phrases and extracts relations between adjective phrases and modified-noun phrases with a dependency grammar parser. The grammar has 122 rules. The parser is similar to the Kudo et al.’s parser (2002) using a Cascaded Chunking Model. The builder extracts relations and counts the number of similar/identical patterns. Finally, the builder saves the results as a new Adjective-Relation Database. The database must be built in advance as a preprocess of phrase-relation-based affect analysis. Table 1: Adjective-Relation Database Example Entries Phrase Pattern Customer's Profile ModifiedNoun Adjective Free Text Sex/Gender Age Novel Too novel. Female 30Novel That's novel but