M. Dolores Molina-González

Also published as: M. Dolores Molina-Gonzalez


2019

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SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations
Flor Miriam Plaza-del-Arco | M. Dolores Molina-González | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 13th International Workshop on Semantic Evaluation

Detecting emotions in textual conversation is a challenging problem in absence of nonverbal cues typically associated with emotion, like fa- cial expression or voice modulations. How- ever, more and more users are using message platforms such as WhatsApp or Telegram. For this reason, it is important to develop systems capable of understanding human emotions in textual conversations. In this paper, we carried out different systems to analyze the emotions of textual dialogue from SemEval-2019 Task 3: EmoContext for English language. Our main contribution is the integration of emotional and sentimental features in the classification using the SVM algorithm.

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SINAI at SemEval-2019 Task 5: Ensemble learning to detect hate speech against inmigrants and women in English and Spanish tweets
Flor Miriam Plaza-del-Arco | M. Dolores Molina-González | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 13th International Workshop on Semantic Evaluation

Misogyny and xenophobia are some of the most important social problems. With the in- crease in the use of social media, this feeling ofhatred towards women and immigrants can be more easily expressed, therefore it can cause harmful effects on social media users. For this reason, it is important to develop systems ca- pable of detecting hateful comments automatically. In this paper, we describe our system to analyze the hate speech in English and Spanish tweets against Immigrants and Women as part of our participation in SemEval-2019 Task 5: hatEval. Our main contribution is the integration of three individual algorithms of predic- tion in a model based on Vote ensemble classifier.

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SINAI at SemEval-2019 Task 6: Incorporating lexicon knowledge into SVM learning to identify and categorize offensive language in social media
Flor Miriam Plaza-del-Arco | M. Dolores Molina-González | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 13th International Workshop on Semantic Evaluation

Offensive language has an impact across society. The use of social media has aggravated this issue among online users, causing suicides in the worst cases. For this reason, it is important to develop systems capable of identifying and detecting offensive language in text automatically. In this paper, we developed a system to classify offensive tweets as part of our participation in SemEval-2019 Task 6: OffensEval. Our main contribution is the integration of lexical features in the classification using the SVM algorithm.

2016

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Domain Adaptation of Polarity Lexicon combining Term Frequency and Bootstrapping
Salud María Jiménez-Zafra | Maite Martin | M. Dolores Molina-Gonzalez | L. Alfonso Ureña-López
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2013

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Bilingual Experiments on an Opinion Comparable Corpus
Eugenio Martínez-Cámara | M. Teresa Martín-Valdivia | M. Dolores Molina-González | L. Alfonso Ureña-López
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis