# A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce
## 3 Materials and Methods
### Data Gathering and Annotation  
As stated above, with the advancement of internet technologies, customers have been sharing a vast amount of data online, expressing their views about various products and services. Companies can leverage these data to enhance their products or services, thereby improving customer satisfaction. To understand customer experiences, preferences, and pain points, we collected and manually annotated 3500 customer feedback from Trustpilot, a leading web platform for sharing user feedback. The data span from 2013 to 2021 and cover five major e-commerce platforms, as shown in Figure 1: Zalando, Wish, Sheinside, Boozt, and Nelly. Prior to data annotation, we performed several steps to detect frequent sets of the aspects mentioned in the text, as our goal was to extract both previously identified aspects from the literature and aspects unique to our corpus. First, we applied part-of-speech tagging to all the reviews. Next, we constructed five bi-term combination types, as suggested by [38], (Noun, Adjective), (Noun, Verb), (Noun, Adverb), (Verb, Adjective), and (Verb, Adverb). Finally, we identified the aspects by counting the most frequent bi-term combinations. Before starting the manual annotation for the entire dataset, we manually annotated 300 reviews to cross-check the quality of the selected aspects. Finally, we employed two independent annotators (the authors of this paper) to manually annotate our dataset, splitting the data into seven similar parts to ensure reliability. Each annotator worked independently on their assigned parts without access to the other's work until completion. After annotating each part, we held discussions to resolve any disagreements and reach a consensus. If a consensus could not be reached, we removed those data points to maintain the integrity and reliability of the dataset. We annotated the data based on whether the sentiments toward the identified aspects were positive or negative. Additionally, we labeled the overall sentiments of the text as positive, negative, or mixed. As presented by [39], the final aspects used in our study are shown in Table 1. Each aspect-review pair was evaluated and annotated according to the presence/absence of the aspect and the corresponding sentiment value. We assigned one of the three following categories for each aspect-review pair: 'positive', 'negative', or 'not mentioned'. This reflects how the data were planned to be processed and analyzed in the study. As an example, we can consider the following review: _\"Wish is great. Finds some very interesting stuff as well as great finds. Just wish delivery did not take so long. Being small business owner I wait on supplies so long and in some occasions did not get them at all. Refunds are h  
Figure 1: Distribution of reviews per shop. great policy just sometimes I do not want a refund I want the products. Other that Wish is great. I am still a customer and fan. Thank you Wish\"._ In this case, the aspects Trust, Refund_process, and Product_availability were assigned the value 'positive', the aspect Shipping was assigned the value 'negative', and all the other aspects were assigned the value 'not mentioned'. We assigned the numerical values 2, 1, and \(-1\) for the three categories. After performing the manual annotation, for this study, we selected a final dataset of 919 customer feedback entries, where the trust aspect, along with its sentiment, were mentioned in the text. Figure 2 displays the sentiment distribution for various aspects of the final dataset, sorted by the total count of sentiments. The aspects are categorized by their positive (green) and negative (blue) mentions. \"Shipping\" had the highest number of mentions, with a significant portion being positive. \"Item quality\" and \"Customer service\" also had high counts, showing a mix of positive and negative sentiments. Other aspects like \"Refund process\", \"Product availability\", and \"Product features\" had fewer mentions but still showed a notable distribution of sentiments.