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SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations

2019-06-01 · SEMEVAL 2019 6 · Flor Miriam Plaza-del-Arco, M. Dolores Molina-Gonz{\'a}lez, Maite Martin, L. Alfonso Ure{\~n}a-L{\'o}pez

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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Tasks

Emotion Classification General Classification

Methods 이 논문이 사용한 방법론

SVM A Support Vector Machine, or SVM, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs…

SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets

2018-06-01 · SEMEVAL 2018 6 · Flor Miriam Plaza-del-Arco, Salud Mar{\'\i}a Jim{\'e}nez-Zafra, Maite Martin, L. Alfonso Ure{\~n}a-L{\'o}pez

Emotion classification is a new task that combines several disciplines including Artificial Intelligence and Psychology, although Natural Language Processing is perhaps the most challenging area. In this paper, we descri…

Emotion Classification Emotion Recognition General Classification

UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

2018-06-01 · SEMEVAL 2018 6 · Pavel P{\v{r}}ib{\'a}{\v{n}}, Tom{\'a}{\v{s}} Hercig, Ladislav Lenc

This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English,…

Classification General Classification Ordinal Classification POS+2

CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons

2018-06-01 · SEMEVAL 2018 6 · Raj Kumar Gupta, Yinping Yang

While sentiment and emotion analysis has received a considerable amount of research attention, the notion of understanding and detecting the intensity of emotions is relatively less explored. This paper describes a syste…

Emotion Classification Emotion Recognition Sentiment Analysis

NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning

2018-04-18 · SEMEVAL 2018 6 · Christos Baziotis, Nikos Athanasiou, Alexandra Chronopoulou, Athanasia Kolovou 외

In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets". We participated in all subtasks for English tweets. We propose a Bi-LSTM architecture equipped with…

Emotion Classification Transfer Learning Word Embeddings

EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning

2019-06-01 · SEMEVAL 2019 6 · Hani Al-Omari, Malak Abdullah, Nabeel Bassam

Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes a participatin…

Word Embeddings