• Korean Journal of Cognitive Science
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• v.27 no.2
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• pp.247-273
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• 2016

Previous studies about lying were mainly executed in pressure situations, such as interviews or crime statements, which made people stressed. This study analyzed deceptive and non-deceptive writings in non-pressure situation through K-LIWC program, in which lies are rarely disclosed and hardly damage the liar even upon disclosure, Also, we compared these results with existing studies on lying. On both writing tasks, there were fewer first-person singular pronouns in deceptive writings than in the non-deceptive writings. The variables indicating cognitive complexity were less used by deceptive writings than by non-deceptive writings in first topic, but in the second topic, more were used by deceptive writings than true writings. In particular, previous studies claim that lies contain more negative emotional words while this report shows that lies in non-pressure situations contains more positive and fewer negative emotional words compared to truth. This finding implies that a situation influences the liar's psychological statement, which changes the contents of the lie.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.10-27
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• 2018

The purpose of this study is to explore the meaning of happiness and unhappiness in Korean society through text mining analysis. Similar words with keywords(happiness/unhappiness) from online news portal are extracted using Word2Vec and TF-IDF method. We also use the K-LIWC dictionary to perform the sentiment analysis of words associated with happiness and unhappiness. In TF-IDF analysis, happiness and unhappiness are highly related to social factors and social issues of the year. In Word2Vec analysis, 'Hope' has been similar with happiness for six years. In K-LIWC analysis, 'money/financial issues', 'school', 'communication' is highly related with happiness and unhappiness. In addition, 'physical condition and symptom' is highly related to unhappiness. Implications, limitations, and suggestions for future research are also discussed.

• Korean Journal of Child Studies
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• v.29 no.3
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• pp.357-373
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• 2008

This study compared the writing of Korean and Korean-Chinese adolescents using K-LIWC (Korean-Linguistic Inquiry Word Count Lee & Yoon, 2005). Three hundred ten (70 : Ulsan, Korea 90 : Yanji, and 150 : Shenyang, China) middle school students wrote a self introductory essay for unknown friends. K-LIWC yielded counts and percentages of word categories using the parts of speech of the Korean language and psychological (emotional, cognitive, sensory/perceptual, social, physical/functional and metaphysical processes) criteria. Results showed that use of pre-noun and present tense correlated with negative mood of the subjects. The writings of Korean-Chinese in Shenyang showed the most negative emotions among the three groups. This was interpreted to be a reflection of better protective factors for Korean-Chinese adolescents in Yanji compared with Shenyang.

• Korean Journal of Cognitive Science
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• v.16 no.2
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• pp.93-121
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• 2005

Substantial amounts of research have been accumulated by the attempt to use linguistic styles as the dependent measure in conducting psychological research. This research was condoned to develope a Korean text analysis program(KLIWC) based on the English text analysis program, LIWC(Linguistic Inquiry and Word Count), and the program reflects the Korean linguistic characteristics and culture that is related with language. We made it possible to analyze agglutinative phrase of many morphemes by linguistic tagging, and basic form dictionary and inflection rule were built. In addition, the face-saving weeds and emotional words were included as the analysis variables. The process of development and characteristics of Korean text analysis have been reviewed, and future direction for the improvement of the program has been discussed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.261-262
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• 2012

소셜 네트워크의 부상과 함께 소셜 네트워크를 이용하여 홍보를 하는 소셜 커머스 시장도 커지고 있다. 소셜 커머스의 경우 일정한 인원 이상이 구입을 해야 거래가 성립한다. 그래서 실질적으로 환불이나 반품이 힘들기 때문에 그만큼 상품평이 구매에 미치는 영향이 크다고 볼 수 있다. 하지만 이러한 상품평의 경우에도 개인의 상황이나 취향 등에 따라 상품평이 주는 정보의 방향이 크게 바뀔 수 있다는 단점도 있다. 본 논문에서는 오피니언 마이닝을 이용하여 의미를 추출하고, LIWC를 통해 사용자의 기본 정보 및 심리 등을 파악하여 보다 정확한 고객의 개인별 상황에 맞는 상품 평점을 제시한다.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.626-630
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• 2019

After several significant nuclear accidents, public attitudes toward nuclear energy technologies and facilities are considered to be one of the essential factors in the national energy and electricity policy-making process of several nations that employ nuclear energy as their key energy resource. However, it is difficult to explore and capture such an attitude, because the majority of prior studies analyzed public attitudes with a limited number of respondents and fragmentary opinion polls. In order to supplement this point, this study suggests a big data analyzing method with K-LIWC (Korean-Linguistic Inquiry and Word Count), sentiment and query analysis methods, and investigates public attitudes, positive and negative emotional statements about nuclear energy with the collected data sets of well-known social media and network services in Korea over time. Results show that several events and accidents related to nuclear energy have consistent or temporary effects on the attitude and ratios of the statements, depending on the kind of events and accidents. The presented methodology and the use of big data in relation to the energy industry is suggested as it can be helpful in addressing and exploring public attitudes. Based on the results, implications, limitations, and future research areas are presented.

• The Journal of Information Systems
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• v.27 no.3
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• pp.203-225
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• 2018

Purpose This study investigated the impact of review language style (affective vs. cognitive) on review helpfulness and the moderating effects of the types of attractions in the relationships between the review language and its helpfulness. Design/methodology/approach This study investigates the impact of review language style (affective vs. cognitive) on review helpfulness and the moderating effects of the types of attractions in the relationships between the review language and its helpfulness. This study selected two hedonic and utilitarian attractions (Hedonic: Brandenburg Gate, Utilitarian: Peragamon Museum) located in Berlin. A total of 3,320 reviews was collected from TripAdvisor. We divided online reviews posted for these places into reviews with more affective language and with more cognitive language by using the LIWC. Then, we investigated the impact of language effect on review helpfulness across the attraction type. Findings The findings suggest that peers tend to judge more helpful toward cognitive language in attraction reviews regardless of attraction type. This study found that peers tend to perceive more helpful toward cognitive review in utilitarian attractions. Even though there was an interaction effect between review language and attraction type, in hedonic attractions, the influence of cognitive language was reduced, but still cognitive reviews would get more helpful votes.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.213-216
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• 2018

As the advantages of smart devices based on artificial intelligence and voice recognition become more prominent, Virtual Assistant is gaining popularity. Virtual Assistant provides a user experience through smart speakers and is valued as the most user friendly IoT device by consumers. The purpose of this study is to investigate whether there are differences in people's perception of the key virtual assistant brand voice recognition. We collected tweets that included six keyword form three companies that provide Virtual Assistant services. The authors conducted semantic network analysis for the collected datasets and analyzed the feelings of people through sentiment analysis. The result shows that many people have a different perception and mainly about the functions and services provided by the Virtual Assistant and the expectation and usability of the services. Also, people responded positively to most keywords.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.63-82
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• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.