Samira Zad

Florida International University

Lecture Information:
  • March 30, 2022
  • 12:00 AM

Speaker Bio

Samira Zad is a Ph.D. candidate working on Natural Language Processing (NLP) and Machine Learning under the supervision of Dr. Mark Finlayson at Knight Foundation School of Computing and Information Sciences, Florida International University since 2017. She has held a prestigious DHS-STEM fellowship through FIU’s DHS-funded Center for Advancing Education and Critical Infrastructure Resilience (CAESCIR), which has fully supported her studies. She has ten conference papers published by ACL and IEEE Workshops and Conferences and two papers under review. She did her Bachelor’s in Mathematics in Iran. Her Master’s is in Computer Science at Northeastern Illinois University. She did an internship under the supervision of Dr. Jana Designer at University of Illinois at Urbana-Champaign in Summer 2018. She is a journal reviewer for ACM, Knowledge-Based Systems and Cognitive Computation, International Journal of Data Science and Analytics, and IEEE Transactions on Neural Networks. Her interests include animate beings’ emotion detection in narrative and application of machine learning techniques in NLP.


Identifying emotions as expressed in text (a.k.a. text emotion recognition) has received a lot of attention over the past decade. Narratives often involve a great deal of emotional expression, and so emotion recognition in narrative text is of great interest to computational approaches to narrative understanding. The meaning and impact of narratives is strongly bound up with the emotions expressed therein. Emotions may be experienced by characters in a story (which may include the narrator), by a story-external narrator, or by the reader.

There have been so far two separate streams of work relevant to this observation: (1) emotion detection and (2) detection of animate beings. This dissertation combined the two streams to construct a computational framework for detecting the emotions experienced by animate beings in a given text. I designed a high-performing approach to emotion recognition in narrative text on Plutchik’s emotion model (joy, sadness, anger, fear, surprise, anticipation, trust, and disgust), identified and semi-automatically improved an emotion lexicon to be used for my animate being’s emotion detection system, provided the ABBE corpus—Animate Beings Being Emotional—a new double-annotated corpus of texts, and demonstrated an emotion detection system based on machine learning to identify the emotions expressed as being experienced by animate beings.