Iuliana Alexandra Fleşcan-Lovin-Arseni

Also published as: Iuliana-Alexandra Flescan-Lovin-Arseni, Iuliana Alexandra Fleșcan-Lovin-Arseni


2018

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EmoIntens Tracker at SemEval-2018 Task 1: Emotional Intensity Levels in #Tweets
Ramona-Andreea Turcu | Sandra Maria Amarandei | Iuliana-Alexandra Flescan-Lovin-Arseni | Daniela Gifu | Diana Trandabat
Proceedings of The 12th International Workshop on Semantic Evaluation

The „Affect in Tweets” task is centered on emotions categorization and evaluation matrix using multi-language tweets (English and Spanish). In this research, SemEval Affect dataset was preprocessed, categorized, and evaluated accordingly (precision, recall, and accuracy). The system described in this paper is based on the implementation of supervised machine learning (Naive Bayes, KNN and SVM), deep learning (NN Tensor Flow model), and decision trees algorithms.

2017

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#WarTeam at SemEval-2017 Task 6: Using Neural Networks for Discovering Humorous Tweets
Iuliana Alexandra Fleșcan-Lovin-Arseni | Ramona Andreea Turcu | Cristina Sîrbu | Larisa Alexa | Sandra Maria Amarandei | Nichita Herciu | Constantin Scutaru | Diana Trandabăț | Adrian Iftene
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper presents the participation of #WarTeam in Task 6 of SemEval2017 with a system classifying humor by comparing and ranking tweets. The training data consists of annotated tweets from the @midnight TV show. #WarTeam’s system uses a neural network (TensorFlow) having inputs from a Naïve Bayes humor classifier and a sentiment analyzer.