Siavash Kazemian


A Taxonomical NLP Blueprint to Support Financial Decision Making through Information-Centred Interactions
Siavash Kazemian | Cosmin Munteanu | Gerald Penn
Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)

Investment management professionals (IMPs) often make decisions after manual analysis of text transcripts of central banks’ conferences or companies’ earning calls. Their current software tools, while interactive, largely leave users unassisted in using these transcripts. A key component to designing speech and NLP techniques for this community is to qualitatively characterize their perceptions of AI as well as their legitimate needs so as to (1) better apply existing NLP methods, (2) direct future research and (3) correct IMPs’ perceptions of what AI is capable of. This paper presents such a study, through a contextual inquiry with eleven IMPs, uncovering their information practices when using such transcripts. We then propose a taxonomy of user requirements and usability criteria to support IMP decision making, and validate the taxonomy through participatory design workshops with four IMPs. Our investigation suggests that: (1) IMPs view visualization methods and natural language processing algorithms primarily as time-saving tools that are incapable of enhancing either discovery or interpretation and (2) their existing software falls well short of the state of the art in both visualization and NLP.


Evaluating Sentiment Analysis in the Context of Securities Trading
Siavash Kazemian | Shunan Zhao | Gerald Penn
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


Evaluating Sentiment Analysis Evaluation: A Case Study in Securities Trading
Siavash Kazemian | Shunan Zhao | Gerald Penn
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis