PA3 will examine the antecedents, rationales, policies and practices of EDI in the energy sector as reflected in a select number of academic and trade journals and relevant web sites. Data will be gathered and analysed using Natural Language Processing (NLP) techniques. NLP techniques include: sentiment analysis (using either LIWC, IBM's NLUS, XLNet or BERT) to identify the sentiment behind the text; topic detection (to identify the common topics); language detection, and key phrase extraction. The aim is to gain insights on how researchers and academics in the energy community present themselves, their expertise and credentials and to what extent they distinguish themselves based on their demographic attributes. PA3 will give insights into the affective, rational, and material development of EDI discourses in the energy literature.

You can watch the project update from the Annual Event in March 2024 on our YouTube Channel.  Gender dynamics and linguistic biases in the energy sector presented by Azhar Nurgaliyeva