Textile sector is one of the most polluting and waste generating sectors. Pertinent efforts have been made to reduce its environmental impact and bring sustainability to operations. The government is continuously promoting patent granting and filing in this sector to develop innovative solutions for sustainability. Hence, there is a continuous increase in the number of patents published in the last decade. These patents are commonly described in text formats that can be analyzed by Artificial Intelligence tools such as Natural Language Processing. In this study, analysis of patents in textile field is conducted to extract keywords from patent abstracts by algorithms such as TF-IDF and TextRank. Identified keywords are used for weight computation and string matching. Weight computation is done by calculating the frequency of each term using TF-IDF and TextRank, whereas string matching is done manually to find repetition of terms. Finally, network analysis is performed to understand the relations between keywords and to find the most influential technologies or innovations for sustainability in textile sector.