## 3 Material and Methods
### Text Pre-Processing  
Text pre-processing is an important step in Natural Language Processing (NLP), because it may affect the final performance of the text mining [34]. Accordingly, textual data obtained from PDF files were pre-processed in two steps: (1) splitting the text into sentences, and (2) eliminating special characters that are not on keyboards. The text was split into sentences using the'sent_toknize' module in NLTK [35], one of the most popular NLP libraries in Python. The tokenizer splits the text into a list of sentences by using a pre-trained algorithm for English. It recognizes words that start sentences and words that do not end sentences such as 'Mr.' and 'PhD.'. This is then followed by replacing all characters other than numbers, alphabets, and special characters that are on keyboards with spaces. A total of 109,173 sentences from 60 sustainability reports and 641 representative sentences from 17 SDGs were obtained for vectorization. Tables 3 and 4 show the first and last five sentences of the sentence list that we obtained after pre-processing.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Social** & & & \\ \hline
**Equity** & & & **Social Development** \\ \hline Goal 4 & Quality education & Goal 11 & Sustainable cities and communities \\ Goal 5 & Gender equality & Goal 16 & Peace justice and strong institutions \\ Goal 10 & Reduced inequalities & Goal 17 & Partnerships for the goals \\ \hline \hline
**Economic** & & & \\ \hline
**Life** & & & **Economic and Technological development** \\ \hline Goal 1 & No poverty & & Goal 8 & Decent work and economic growth \\ Goal 2 & Zero hunger & & Goal 9 & Industry, innovation, and infrastructure \\ Goal 3 & Good health and well-being & & \\ \hline \hline
**Environmental** & & & \\ \hline
**Resources** & & & **Environments** \\ \hline Goal 6 & Clean water and sanitation & & \\ Goal 7 & Affordable and clean energy & & \\ Goal 12 & Responsible consumption & & Goal 13 & Climate action \\  & and production & & Goal 15 & Life on land \\ Goal 14 & Life below water & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: SGDs in six categories according to human needs.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**No** & **Doc ID** & **File Name** & **Sentence** \\ \hline
0 & 0 & BASF\_2011.pdf & The cover photo shows a Berlin subway during the... \\
1 & 0 & BASF\_2011.pdf & The branding motifs shown in this report, taken from... \\
2 & 0 & BASF\_2011.pdf & You can find this and other publications from BAS... \\
3 & 0 & BASF\_2011.pdf & The cover photo shows a Berlin subway during the... \\
4 & 0 & BASF\_2011.pdf & The branding motifs shown in this report, taken from... \\... &... &... &... \\
109168 & 59 & Walmart\_2020.pdf & Additional methodology information can be found in our... \\
109169 & 59 & Walmart\_2020.pdf & 36 This metric has been adjusted to account for the... \\
109170 & 59 & Walmart\_2020.pdf & In other words, Brazil s energy use and square footage... \\
109171 & 59 & Walmart\_2020.pdf & The adjusted baseline result is 11.25\% vs. 2010. \\
109172 & 59 & Walmart\_2020.pdf & The unadjusted result (with Brazil still included in... \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sentences obtained from sustainability reports of 6 companies.