## 3 A Method to Identify the Usefulness of Online Reviews
### Feature Extraction Based on Grounded Theory  
In the stage of data preprocessing, the online comment text should be processed with duplicate removal, word segmentation, and stop words removal, and the pre-processed online comment text data set can be obtained. In the stage of constructing the feature representation of online reviews, 35 experienced online shoppers are interviewed through semi-structured interviews. The interview outline is shown in Table 1. Secondly, based on the Grounded theory, open coding, spindle coding, and selective coding are carried out for the semi-structured interview content [26], and feature selection and feature combination are carried out for the coding results considering the purchasing decision-making process of consumers [27]. The decision-making process of consumers is divided into several stages: demand generation, information collection, product evaluation, purchase decision, and post-purchase behavior [16]. Therefore, the whole semi-structured interview process is also centered on the decision-making process of consumers. A total of 29 (P\({}_{1}\)-P\({}_{29}\)) complete interview texts are collected in this study, while the rest were incomplete or not completed as required. After the interview, the audio recordings were transcribed into 29 interview texts with more than 20,000 words. Through data coding analysis of the above interview texts, the feature selected in the final stage of demand generation is keyword retrieval number (N\({}_{\text{kr}}\)); in the information collection stage, the selected characteristic is the number of comments viewed (N\({}_{\text{cv}}\)); in the product evaluation stage, the selected characteristics are the number of other products to be viewed in the store where the target located (N\({}_{\text{PSP}}\)) and the number of similar products of the target to be viewed (N\({}_{\text{PP}}\)); in the purchase decision stage, the selected characteristic is the number of days the product was added to the cart (D\({}_{\text{p}}\)); in the post-purchase behavior stage, the selected characteristics are the days between receiving goods and commenting (D\({}_{\text{rc}}\)), the days between first comments and additional comments (D\({}_{\text{FA}}\)), the number of comment to be viewed (N\({}_{\text{B}}\)), and the number of likes on comments (N\({}_{\text{G}}\)). The details are shown in Figure 2. \begin{table}
\begin{tabular}{c c} \hline
**Number** & **Interview Questions** \\ \hline
1 & How many hours a week do you spend reading reviews on e-commerce platforms? \\ What is the frequency? \\
2 & Could you tell me something about your usual shopping process? \\
3 & How do you select search keywords for products? \\
4 & What shopping experience would you most like to see in a product review? \\
5 & What metrics do you look for when reviewing reviews? \\
6 & Do you compare the target products you want to buy? How does it compare? \\
7 & Have you ever collected or added the products you finally purchased? \\
8 & How long do you comment on the product firstly and additionally after receiving it? \\ \hline \end{tabular}
\end{table} 
Table 1: Interview outline. 1. \(\mathrm{N_{kr}}\): refers to the keyword retrieval number. According to the interview record of Question 3 in P2, _\"Generally, when I want to buy seasonal clothes, I will search several keywords, such as '2022 new women's clothing', '2022 winter extra thick', and 'blouse loose for women', to find clothes suitable for me\"_;
2. \(\mathrm{N_{cv}}\): refers to the number of comments viewed. P14 indicates that he will browse the reviews of the target products that he wants to buy, especially for the reviews that contain the product features that he/she is interested in;
3. \(\mathrm{N_{psp}}\): refers to the number of other products to be viewed in the store where the target is located. The interview record for P16 for question 2 records that _\"when I find something I want to buy, I will click it to see the reviews and the stores, especially when I buy clothes.