Aspect-Based Sentiment Analysis Using BERT: Developing Aspect Category Sentiment Classification Models (BERT를 활용한 속성기반 감성분석: 속성카테고리 감성분류 모델 개발)
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- Journal of Intelligence and Information Systems
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- v.26 no.4
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- pp.1-25
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- 2020
Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.
Digital economy has grown rapidly so that the new business area called 'Internet business' has been dramatically extended as time goes on. However, in the case of Internet business, market shares of individual companies seem to fluctuate very extremely. Thus marketing managers who operate the Internet sites have seriously observed the competition structure of the Internet business market and carefully analyzed the competitors' behavior in order to achieve their own business goals in the market. The newly created Internet business might differ from the offline ones in management styles, because it has totally different business circumstances when compared with the existing offline businesses. Thus, there should be a lot of researches for finding the solutions about what the features of Internet business are and how the management style of those Internet business companies should be changed. Most marketing literatures related to the Internet business have focused on individual business markets. Specifically, many researchers have studied the Internet portal sites and the Internet shopping mall sites, which are the most general forms of Internet business. On the other hand, this study focuses on the entire Internet business industry to understand the competitive circumstance of online market. This approach makes it possible not only to have a broader view to comprehend overall e-business industry, but also to understand the differences in competition structures among Internet business markets. We used time-series data of Internet connection rates by consumers as the basic data to figure out the competition patterns in the Internet business markets. Specifically, the data for this research was obtained from one of Internet ranking sites, 'Fian'. The Internet business ranking data is obtained based on web surfing record of some pre-selected sample group where the possibility of double-count for page-views is controlled by method of same IP check. The ranking site offers several data which are very useful for comparison and analysis of competitive sites. The Fian site divides the Internet business areas into 34 area and offers market shares of big 5 sites which are on high rank in each category daily. We collected the daily market share data about Internet sites on each area from April 22, 2008 to August 5, 2008, where some errors of data was found and 30 business area data were finally used for our research after the data purification. This study performed several empirical analyses in focusing on market shares of each site to understand the competition among sites in Internet business of Korea. We tried to perform more statistically precise analysis for looking into business fields with similar competitive structures by applying the cluster analysis to the data. The research results are as follows. First, the leading sites in each area were classified into three groups based on averages and standard deviations of daily market shares. The first group includes the sites with the lowest market shares, which give more increased convenience to consumers by offering the Internet sites as complimentary services for existing offline services. The second group includes sites with medium level of market shares, where the site users are limited to specific small group. The third group includes sites with the highest market shares, which usually require online registration in advance and have difficulty in switching to another site. Second, we analyzed the second place sites in each business area because it may help us understand the competitive power of the strongest competitor against the leading site. The second place sites in each business area were classified into four groups based on averages and standard deviations of daily market shares. The four groups are the sites showing consistent inferiority compared to the leading sites, the sites with relatively high volatility and medium level of shares, the sites with relatively low volatility and medium level of shares, the sites with relatively low volatility and high level of shares whose gaps are not big compared to the leading sites. Except 'web agency' area, these second place sites show relatively stable shares below 0.1 point of standard deviation. Third, we also classified the types of relative strength between leading sites and the second place sites by applying the cluster analysis to the gap values of market shares between two sites. They were also classified into four groups, the sites with the relatively lowest gaps even though the values of standard deviation are various, the sites with under the average level of gaps, the sites with over the average level of gaps, the sites with the relatively higher gaps and lower volatility. Then we also found that while the areas with relatively bigger gap values usually have smaller standard deviation values, the areas with very small differences between the first and the second sites have a wider range of standard deviation values. The practical and theoretical implications of this study are as follows. First, the result of this study might provide the current market participants with the useful information to understand the competitive circumstance of the market and build the effective new business strategy for the market success. Also it might be useful to help new potential companies find a new business area and set up successful competitive strategies. Second, it might help Internet marketing researchers take a macro view of the overall Internet market so that make possible to begin the new studies on overall Internet market beyond individual Internet market studies.
The interplay between hedonic and utilitarian attributes has assumed special significance in recent years; it has been proposed that consumption offerings should be viewed as experiences that stimulate both cognitions and feelings rather than as mere products or services. This research builds on previous work on hedonic versus utilitarian benefits, regulatory focus theory, customer satisfaction to address two question: (1) Is the shopping goal at the point of purchase different from the shopping value? and (2) Is the customer loyalty after the use different from the shopping value and shopping goal? We surveyed 345 peoples those who have bought the electronic-goods within 6 months. This research dealt with the shopping value which is consisted of 2 types, hedonic and utilitarian. Those who pursue the hedonic shopping value may prefer the pleasure of purchasing experience to the product itself. They tend to prefer atmosphere, arousal of the shopping experience. Consistent with previous research, we use the term "hedonic" to refer to their aesthetic, experiential and enjoyment-related value. On the contrary, Those who pursue the utilitarian shopping value may prefer the reasonable buying. It may be more functional. Consistent with previous research, we use the term "utilitarian" to refer to the functional, instrumental, and practical value of consumption offerings. Holbrook(1999) notes that consumer value is an experience that results from the consumption of such benefits. In the context of cell phones for example, the phone's battery life and sound volume are utilitarian benefits, whereas aesthetic appeal from its shape and color are hedonic benefits. Likewise, in the case of a car, fuel economics and safety are utilitarian benefits whereas the sunroof and the luxurious interior are hedonic benefits. The shopping goals are consisted of the promotion focus goal and the prevention focus goal, based on the self-regulatory focus theory. The promotion focus is characterized into focusing ideal self because they are oriented to wishes and vision. The promotion focused individuals are tend to be more risk taking. They are more sensitive to hope and achievement. On the contrary, the prevention focused individuals are characterized into focusing the responsibilities because they are oriented to safety. The prevention focused individuals are tend to be more risk avoiding. We wanted to test the relation among the shopping value, shopping goal and customer loyalty. Customers show the positive or negative feelings comparing with the expectation level which customers have at the point of the purchase. If the result were bigger than the expectation, customers may feel positive feeling such as delight or satisfaction and they would want to share their feelings with other people. And they want to buy those products again in the future time. There is converging evidence that the types of goals consumers expect to be fulfilled by the utilitarian dimension of a product are different from those they seek from the hedonic dimension (Chernev 2004). Specifically, whereas consumers expect the fulfillment of product prevention goals on the utilitarian dimension, they expect the fulfillment of promotion goals on the hedonic dimension (Chernev 2004; Chitturi, Raghunathan, and Majahan 2007; Higgins 1997, 2001) According to the regulatory focus theory, prevention goals are those that ought to be met. Fulfillment of prevention goals in the context of product consumption eliminates or significantly reduces the probability of a painful experience, thus making consumers experience emotions that result from fulfillment of prevention goals such as confidence and securities. On the contrary, fulfillment of promotion goals are those that a person aspires to meet, such as "looking cool" or "being sophisticated." Fulfillment of promotion goals in the context of product consumption significantly increases the probability of a pleasurable experience, thus enabling consumers to experience emotions that result from the fulfillment of promotion goals. The proposed conceptual framework captures that the relationships among hedonic versus utilitarian shopping values and promotion versus prevention shopping goals respectively. An analysis of the consequence of the fulfillment and frustration of utilitarian and hedonic value is theoretically worthwhile. It is also substantively relevant because it helps predict post-consumption behavior such as the promotion versus prevention shopping goals orientation. Because our primary goal is to understand how the post consumption feelings influence the variable customer loyalty: word of mouth (Jacoby and Chestnut 1978). This research result is that the utilitarian shopping value gives the positive influence to both of the promotion and prevention goal. However the influence to the prevention goal is stronger. On the contrary, hedonic shopping value gives influence to the promotion focus goal only. Additionally, both of the promotion and prevention goal show the positive relation with customer loyalty. However, the positive relation with promotion goal and customer loyalty is much stronger. The promotion focus goal gives the influence to the customer loyalty. On the contrary, the prevention focus goal relates at the low level of relation with customer loyalty than that of the promotion goal. It could be explained that it is apt to get framed the compliment of people into 'gain-non gain' situation. As the result, for those who have the promotion focus are motivated to deliver their own feeling to other people eagerly. Conversely the prevention focused individual are more sensitive to the 'loss-non loss' situation. The research result is consistent with pre-existent researches. There is a conceptual parallel between necessities-needs-utilitarian benefits and luxuries-wants-hedonic benefits (Chernev 2004; Chitturi, Raghunathan and Majaha 2007; Higginns 1997; Kivetz and Simonson 2002b). In addition, Maslow's hierarchy of needs and the precedence principle contends luxuries-wants-hedonic benefits higher than necessities-needs-utilitarian benefits. Chitturi, Raghunathan and Majaha (2007) show that consumers are focused more on the utilitarian benefits than on the hedonic benefits of a product until their minimum expectation of fulfilling prevention goals are met. Furthermore, a utilitarian benefit is a promise of a certain level of functionality by the manufacturer or the retailer. When the promise is not fulfilled, customers blame the retailer and/or the manufacturer. When negative feelings are attributable to an entity, customers feel angry. However in the case of hedonic benefit, the customer, not the manufacturer, determines at the time of purchase whether the product is stylish and attractive. Under such circumstances, customers are more likely to blame themselves than the manufacturer if their friends do not find the product stylish and attractive. Therefore, not meeting minimum utilitarian expectations of functionality generates a much more intense negative feelings, such as anger than a less intense feeling such as disappointment or dissatisfactions. The additional multi group analysis of this research shows the same result. Those who are unsatisfactory customers who have the prevention focused goal shows higher relation with WOM, comparing with satisfactory customers. The research findings in this article could have significant implication for the personal selling fields to increase the effectiveness and the efficiency of the sales such that they can develop the sales presentation strategy for the customers. For those who are the hedonic customers may be apt to show more interest to the promotion goal. Therefore it may work to strengthen the design, style or new technology of the products to the hedonic customers. On the contrary for the utilitarian customers, it may work to strengthen the price competitiveness. On the basis of the result from our studies, we demonstrated a correspondence among hedonic versus utilitarian and promotion versus prevention goal, WOM. Similarly, we also found evidence of the moderator effects of satisfaction after use, between the prevention goal and WOM. Even though the prevention goal has the low level of relation to WOM, those who are not satisfied show higher relation to WOM. The relation between the prevention goal and WOM is significantly different according to the satisfaction versus unsatisfaction. In addition, improving the promotion emotions of cheerfulness and excitement and the prevention emotion of confidence and security will further improve customer loyalty. A related potential further research could be to examine whether hedonic versus utilitarian, promotion versus prevention goals improve customer loyalty for services as well. Under the budget and time constraints, designers and managers are often compelling to choose among various attributes. If there is no budget or time constraints, perhaps the best solution is to maximize both hedonic and utilitarian dimension of benefits. However, they have to make trad-off process between various attributes. For the designers and managers have to keep in mind that without hedonic benefit satisfaction of the product it may hard to lead the customers to the customer loyalty.