Ioan-Bogdan Iordache


2022

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Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism
Ștefan Cobeli | Ioan-Bogdan Iordache | Shweta Yadav | Cornelia Caragea | Liviu P. Dinu | Dragoș Iliescu
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Finding the polarity of feelings in texts is a far-reaching task. Whilst the field of natural language processing has established sentiment analysis as an alluring problem, many feelings are left uncharted. In this study, we analyze the optimism and pessimism concepts from Twitter posts to effectively understand the broader dimension of psychological phenomenon. Towards this, we carried a systematic study by first exploring the linguistic peculiarities of optimism and pessimism in user-generated content. Later, we devised a multi-task knowledge distillation framework to simultaneously learn the target task of optimism detection with the help of the auxiliary task of sentiment analysis and hate speech detection. We evaluated the performance of our proposed approach on the benchmark Optimism/Pessimism Twitter dataset. Our extensive experiments show the superior- ity of our approach in correctly differentiating between optimistic and pessimistic users. Our human and automatic evaluation shows that sentiment analysis and hate speech detection are beneficial for optimism/pessimism detection.

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Investigating the Relationship Between Romanian Financial News and Closing Prices from the Bucharest Stock Exchange
Ioan-Bogdan Iordache | Ana Sabina Uban | Catalin Stoean | Liviu P. Dinu
Proceedings of the Thirteenth Language Resources and Evaluation Conference

A new data set is gathered from a Romanian financial news website for the duration of four years. It is further refined to extract only information related to one company by selecting only paragraphs and even sentences that referred to it. The relation between the extracted sentiment scores of the texts and the stock prices from the corresponding dates is investigated using various approaches like the lexicon-based Vader tool, Financial BERT, as well as Transformer-based models. Automated translation is used, since some models could be only applied for texts in English. It is encouraging that all models, be that they are applied to Romanian or English texts, indicate a correlation between the sentiment scores and the increase or decrease of the stock closing prices.

2021

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A Computational Exploration of Pejorative Language in Social Media
Liviu P. Dinu | Ioan-Bogdan Iordache | Ana Sabina Uban | Marcos Zampieri
Findings of the Association for Computational Linguistics: EMNLP 2021

In this paper we study pejorative language, an under-explored topic in computational linguistics. Unlike existing models of offensive language and hate speech, pejorative language manifests itself primarily at the lexical level, and describes a word that is used with a negative connotation, making it different from offensive language or other more studied categories. Pejorativity is also context-dependent: the same word can be used with or without pejorative connotations, thus pejorativity detection is essentially a problem similar to word sense disambiguation. We leverage online dictionaries to build a multilingual lexicon of pejorative terms for English, Spanish, Italian, and Romanian. We additionally release a dataset of tweets annotated for pejorative use. Based on these resources, we present an analysis of the usage and occurrence of pejorative words in social media, and present an attempt to automatically disambiguate pejorative usage in our dataset.