Target-Aspect-Sentiment Joint Detection with CNN Auxiliary Loss for Aspect-Based Sentiment Analysis (CNN 보조 손실을 이용한 차원 기반 감성 분석)
-
- Journal of Intelligence and Information Systems
- /
- v.27 no.4
- /
- pp.1-22
- /
- 2021
Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.
Despite the voluminous research on switching barriers, the notion that they can generate negative responses has not been investigated. Further, a critical question is what determines the strength of such negative responses. To address this question, the classic theory of psychological reactance is briefly reviewed, and the idea of switching barrier is advanced. This study attempts to suggest a model on the negative effects of switching- frustrated situation, based on the studies on psychological reactance. According to psychological reactance theory(Brehm 1966), whenever a freedom is threatened or removed, individuals are motivated, at least temporarily, to restore their freedom. For example, if individuals think they are free to engage in behaviors .v, y, or z, then threatening their freedom to engage in x would cause psychological reactance. This reactance could be reduced by an increase in the perceived attractiveness of engaging in, the threatened behavior(Kivetz 2005). This investigation seeks to extend existing switching barrier research in three important ways. First, while the past research has emphasized only positive role of switching barrier, this study address negative role of it by applying psychological reactance theory. Second, to find negative results of switching barrier, I suggest negative psychological response including regret to the past choice, resentment to the present provider, and strong desire to the alternative provider. Third, I suggest the perceived severity of the switching barriers, the attractiveness of the alternative as switching-frustrated situation which can lead to negative results. And, in addition to these relationships, I added moderated effects of perceived justice for better explanation. So this study includes the following hypotheses. H1-1 ~ H1-3: The attractiveness of the alternative has a positive effect regret to the past choice (h1-1), resentment to the present provider (h1-2), and strong desire to the alternative provider (h1-3). H2-1 ~ H2-3 : The perceived severity of the switching barrier has a positive effect regret to the past choice (h2-1), resentment to the present provider (h2-2), and strong desire to the alternative provider (h2-3). H3-1 ~ H3-3 : The positive relationships between the attractiveness of the alternative and consumer' negative responses will be stronger at low level of perceived justice than at high level of perceived justice. H4-1 ~ H4-3 : The positive relationships between the perceived severity of the switching barrier and consumer' negative responses will be stronger at low level of perceived justice than at high level of perceived justice. Survey research is employed to test hypotheses involving perceived severity of the switching barrier(Hess 2008), attractiveness of the alternative(Anderson and Narus 1990; Ohanian 1990),regret(Glovich and Medvec 1995), resentment, strong desire(Alcohol Urge Questionaire: Bohn et al. 1995), perceived justice(Bies and Moag 1986; Clemmer 1993; Lind and Tyler 1998). Previous researches, such as reactance theory, emotion and service failure, have been referenced to measure constructs. All items were measured on a 7-point Likert scale ranging from "strongly disagree" to "strongly agree". We collected data involving various service field, and used 249 respondents to analyze these data using the moderated regression. The results of our analysis suggest, as expected, that the perceived severity of the switching barrier had positive effects on regret to the past choice(b = .197, p< .01), resentment to the present provider(b = .214, p< .01), and strong desire to the alternative provider(b = .254, p< .001). And the attractiveness of the alternative had positive effects on regret to the past choice(b = .353, p<.001), resentment to the present provider(b = .174, p< .01), and strong desire to the alternative provider(b = .265, p< .001). However, our findings indicate perceived justice partly moderates relationship between switching-frustrated situation and psychological negative response. The study has brought to light a number of insights between switching barriers and consumer' negative responses that have been subject to little prior research. In particular, this study adds to the existing understanding of the psychological responses to switching barriers in switching- frustrated situation. This research therefore has significance to marketers for strategic marketing programs, particularly in terms of customer retention and switching barrier strategies. Since consumers could exhibit negative responses to switching barrier, companies would be able to lose their customer when they thoughtlessly use switching barrier for remaining customer. Although the study has these contributions, there are several limitations including unsupported hypotheses and research method. So, we need to make up for these limitations in the future researches.