Liana Minasyan is an NLP researcher and Machine Learning engineer at Polixis. She is involved in various NLP projects, from classic problems to tackling currently unsolved ones. As part of her job, she is creating tools for transliteration, data mapping, target-based sentiment analysis, text summarization, etc. She was involved in sentiment and emotion recognition research for Krisp in collaboration with TUMO. As her capstone at AUA, with YerevaNN supervision, she has worked on the Western and Eastern Armenian Treebank project and was a contributor to Stanford's Universal Dependencies and their NLP toolkit.
The classic sentiment analysis analyzes texts, images, emojis, etc to know what other people think of a product, service, company, or event. While sentiment analysis can be considered one of the accomplished tasks of Natural Language Processing tasks, more fine-grained types of it like Target Based Sentiment Analysis(TSA) or Aspect-based sentiment analysis(ABSA) are the not quite the same. In TSA we want to see the sentiment of a given text towards a particular entity(in my case person or organization). This task is one of the non-solved ones. With the T5 question answering transformer model it was possible to solve the task with results 20% higher than the current leaderboards.