## 3 Proposed Method  
To perform learning in the proposed method, approximately 35,000 user reviews collected from 25 restaurants and 33 tourist attractions in Ulsan Metropolitan City, South Korea, were used. The user reviews for each place were collected using the blog search APIs [54] provided by Naver, a popular online portal service in South Korea. From the collection process, 58 data sets were prepared, each of which had a set of reviews for a restaurant or a tourist attraction. Tables 2 and 3 show the number of reviews collected from restaurants and tourist attractions, respectively, in each district of Ulsan. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Type** & \begin{tabular}{l} **Ref** \\ **No.** \\ \end{tabular} & \begin{tabular}{l} **Source Data** \\ \end{tabular} & \begin{tabular}{l} **Best Algorithm** \\ \end{tabular} & \
\begin{tabular}{l} **Measure** \\ \end{tabular} &
\begin{tabular}{l} **Best** \\ **Score** \\ \end{tabular} \\ \hline \multirow{9}{*}{Type 1} & [29] & Hotel reviews from AMT & SVM & Accuracy & 0.898 \\  & [30] & Hotel reviews of [29] & SVM & F1 score & 0.840 \\  & [31] & Hotel reviews of [29] & SVM & Accuracy & 0.912 \\  & [32] & 6000 reviews from Epinions & Naïve Bayes & F1 score & 0.631 \\  & [33] & Yelp data & SVM & Accuracy & 0.861 \\  & [34] & Yelp data & SVM & F1 score & 0.850 \\  & [35] & Hotel reviews of [29] & DNN & Accuracy & 0.890 \\  & [36] & Hotel reviews of [31] & CNN & F1 score & 0.823 \\  & [38] & 24,166 hotel reviews & CNN & F1 score & 0.828 \\  & [39] & Hotel reviews of [29] & LSTM & F1 score & 0.946 \\  & [40] & Hotel reviews of [31] & LSTM & F1 score & 0.876 \\  & [41] & Yelp data & Attention + CNN & F1 score & 0.912 \\  & [43] & Hotel reviews of [31] & CNN + RNN & F1 score & 0.839 \\  & [44] & Yelp data & CNN + LSTM & F1 score & 0.870 \\  & [45] & Yelp data & Transformers + CNN + LSTM & F1 score & 0.965 \\ \hline \multirow{2}{*}{Type 2 or 3} & [47] & 470 reviews & Logistic regression & AUC & 0.987 \\  & [48] & 1032 synthesized reviews & SVM & AUC & 0.950 \\ \cline{1-1}  & [11] & Tourist attraction reviews & BERT & F1 score & 0.930 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarization of the existing studies: source data, best algorithm, performance measure, and best detection score. The review data sets were then tailored for learning. The preprocessing steps for learning include labeling, morphological analysis, and stopword removal. First, manual labeling was performed for supervised learning. In this step, each review in the data sets was manually labeled to indicate whether it is irrelevant or not. From the labeling, about 53.7% of the reviews collected from restaurants were found irrelevant, and about 71.6% of the reviews collected from tourist attractions were found irrelevant, as shown in Tables 2 and 3. The words in the reviews were then standardized by conducting morphological analysis. In this step, adjectives or verbs were converted into their basic forms. Note that user reviews often include proper nouns or new words reflecting recent trends, which are not included in a dictionary. Thus, instead of using conventional morphological analysis approaches based on a dictionary, we adopted a statistical approach using cohesion scoring [55; 56], which can be calculated based on word frequencies. After the conversion, the stopwords were removed from the reviews. In this step, meaningless words such as articles were excluded. To improve accuracy, rarely occurring words were also excluded from the reviews. In the proposed method, words with fewer than three occurrences were removed. Tables 4 and 5 show the statistics for the reviews of the restaurants and tourist attractions after the preprocessing, respectively. The number of unique tokens and the maximum number of tokens in a sentence are used for the parameter settings of the detection models that will be implemented below.