## 4 Case Study  
### Data Collection and Sentiment Analysis  
To demonstrate the application of BLCC and DIPA and validate their usefulness, this section presents a case study of a five-star hotel in Seoul, Korea (hereafter called Hotel A). Online reviews in Korean were extracted from Tripadvisor.com (accessed on 9 August 2020), the world's largest travel guidance platform. Among the five-star hotels in Seoul, Hotel A has been reviewed most frequently on the website and was selected as the main analysis target. A total of 980 Korean reviews posted between January 2018 and April 2020 were used in this case study. For this case study, sentiment analysis is conducted using the lexicon-based approach presented by [PERSON] et al. [55], which utilizes predefined lexicons of product attributes and sentiment words tailored to the hotel domain. In the lexicon-based sentiment analysis, the attribute appearance is determined for the predefined set of attributes in the attribute lexicon. The sentiment for an attribute is then determined based on the sentiment words associated with it. The polarity of the sentiment words is detected using the sentiment lexicon, which is a list of sentiment words (mostly adjectives and adverbs), each of which is tagged with the corresponding sentiment category (positive or negative). Although the lexicon-based sentiment analysis has a drawback in that it largely depends on the completeness and quality of the underlying sentiment lexicons, it has several advantages that make it a suitable choice for the case study. First, it does not require training of the model and can be applied to relatively small datasets [56]. Second, sentiment lexicons are relatively easy to modify and expand [57], making it possible to customize them to a particular domain and the specific needs of the decision maker [33; 58]. Third, the approach enables tracking of the results of the sentiment analysis and understanding the root cause of the outcomes [58; 59]. Finally, the approach has been shown to offer good performance when analyzing non-English reviews [58; 60]. Table 5 shows the attribute lexicon employed in the case study, which defines seven attributes related to various aspects of hotel services: furniture and appliances (FA), bathroom condition (BC), room condition (RC), dining and food (DF), service and staff (SS), facility and conveniences (FC), and location and surroundings (LS). For each attribute, corresponding words, including sub-attributes and synonyms, are defined. In total, the attribute lexicon consists of 240 words. In Table 6, the sentiment lexicon applied in the sentiment analysis is presented. Based on the Kunsan National University (KNU) Korean sentiment lexicon [61], the sentiment words that frequently appear in online hotel reviews are classified as positive or negative words. The sentiment lexicon comprises a total of 251 words, with 130 positive sentiment words and 121 negative sentiment words. The sentiment analysis process begins with preprocessing the original review text by splitting it into sentences. Next, tokenizing and part-of-speech tagging are applied to the sentences. In this case study, the Korean morphological analyzer RHINO library [62] was used for this preprocessing. Using the predefined lexicons, the attribute appearance and sentiment polarity are then detected at the sentence level, and the results for each sentence are aggregated at the review level. Table 7 presents the accuracy range of the lexicon-based sentiment analysis, which is between 82% and 89%. Table 8 presents the final output of the sentiment analysis, that is, structured data for the BLCC and DIPA applications.