## 4 Results and Discussion
### Thematic Analysis  
We have six similarity scores per report. Hence, the total number of similarity scores for six companies over 10 years is 360. These similarity scores indicate how heavily each report mentions each of the six categories. Since the scores were converted to 0-100 values before averaging them into six categories, interpretation of the scores as to the relative importance of the different theme is still valid. To confirm the trend of the thematic structure of sustainability reports, we visualized the similarity score data into charts. The visualization is effective in identifying changes and patterns, especially within large data sets. In this chapter, we present the results of the \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mean** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline Keyword matching & 20.72 & 0.03 & 20.67 & 21.02 \\ Sentence similarity & 43.15 & 10.42 & 4.09 & 89.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Similarity score distribution comparison. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Company** & **Country** & **Sentence** & **Label** & **Score** \\ \hline Walmart & US & \begin{tabular}{c} We provide convenient access to high-quality, affordable food and \\ other essential products and services to millions of people \\ each week. \\ \end{tabular} & Positive & 1.00 \\ \cline{3-4} Walmart & US & \begin{tabular}{c} Walmart associates receive ethics training during onboarding and \\ regularly thereafter. \\ \end{tabular} & Positive & 0.74 \\ \cline{3-4}  & \
\begin{tabular}{c} In any given year, an increase or decrease in UPC volume weight \\ disclosures may impact reporting. \\ \end{tabular} & Negative & 0.39 \\ \cline{3-4}  &
\begin{tabular}{c} No one organization can single-handedly transform supply \\ chain systems. \\ \end{tabular} & Negative & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._thematic and sentiment analysis using line with markers charts. For visualization, the most basic and popular Python chart library, Matplotlib [47], was used. For this section, we first closely examined the thematic structure of the sustainability reports and its trend or change for BASF. This is to check whether the sentence similarity analysis result for BASF is consistent with the generally known characteristics of BASF. This can also prove the significance and advantages of the sentence similarity analysis in this study. In Figure 4, we used a line with markers chart to show the thematic structure of BASF's sustainability reports over a 10-year period. The number indicated at the bottom of Figure 4 is the length of each report in terms of the number of sentences. Over the past decade, the length of reports has steadily increased and almost doubled. However, except for some slight changes between 2017 and 2018, the thematic structure of BASF's sustainability reports hardly changed. BASF is one of the world's largest chemical production companies, and its business is closely related to the environment. The category 'Environments' includes SDG goals 13 (Climate Action) and 15 (Life on Land). Therefore, the company is likely to include issues about the impact and risks of business activities on the environment in their reports. As shown in the figure, the similarity scores for the 'Environments' category are noticeably high, while the similarity score for the 'Resources' category is the lowest. However, it is very surprising that the thematic structure for 10 years shows a very monotonous pattern with hardly any change. To check whether a similar monotonous structure and pattern was being implemented in companies belonging to completely different industries, we examined what happened to Nestle, a typical B2C company. In Figure 5, we used a line chart with markers chart to show the thematic structure of Nestle's sustainability reports over a 10-year period. Figure 4: Thematic structure of sustainability reports: BASF.