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 system developed for predicting emotion intensity in tweets. Given a Twitter message, CrystalFeel uses features derived from parts-of-speech, n-grams, word embedding, and multiple affective lexicons including Opinion Lexicon, SentiStrength, AFFIN, NRC Emotion {\&} Hash Emotion, and our in-house developed EI Lexicons to predict the degree of the intensity associated with fear, anger, sadness, and joy in the tweet. We found that including the affective lexicons-based features allowed the system to obtain strong prediction performance, while revealing interesting emotion word-level and message-level associations. On gold test data, CrystalFeel obtained Pearson correlations of 0.717 on average emotion intensity and of 0.816 on sentiment intensity.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Emotion Classification Emotion Recognition Sentiment Analysis

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 …

Emotion Classification General Classification

CSIRO-LT at SemEval-2025 Task 11: Adapting LLMs for Emotion Recognition for Multiple Languages

2025-08-02 · Jiyu Chen, Necva Bölücü, Sarvnaz Karimi, Diego Mollá 외 arxiv

Detecting emotions across different languages is challenging due to the varied and culturally nuanced ways of emotional expressions. The \textit{Semeval 2025 Task 11: Bridging the Gap in Text-Based emotion} shared task w…

Emotion Recognition

LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification

2019-06-01 · SEMEVAL 2019 6 · Waleed Ragheb, J{\'e}r{\^o}me Az{\'e}, S Bringay, ra 외

This paper addresses the problem of modeling textual conversations and detecting emotions. Our proposed model makes use of 1) deep transfer learning rather than the classical shallow methods of word embedding; 2) self-at…

Emotion Detection and Classification General Classification Transfer Learning

LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets

2018-06-01 · SEMEVAL 2018 6 · Luna De Bruyne, Orph{\'e}e De Clercq, V{\'e}ronique Hoste

This paper presents an emotion classification system for English tweets, submitted for the SemEval shared task on Affect in Tweets, subtask 5: Detecting Emotions. The system combines lexicon, n-gram, style, syntactic and…

Emotion Classification Emotion Recognition General Classification Sentiment Analysis

TDBot at SemEval-2019 Task 3: Context Aware Emotion Detection Using A Conditioned Classification Approach

2019-06-01 · SEMEVAL 2019 6 · Sourabh Maity

With the system description it is shown how to use the context information while detecting the emotion in a dialogue. Some guidelines about how to handle emojis was also laid out. While developing this system I realized …

General Classification