SemEval 2018 Task 1

material ID: semeval2018task1

Related Papers

Affect in Tweets using Experts Model

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

Estimating the intensity of emotion has gained significance as modern textual inputs in po- tential applications like social media, retail markets, psychology, advertisementse- etc., carry a lot of emotions, feelings, …...

18713

AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractIn this paper we describe our submission to SemEval-2018 Task 1: Affects in Tweets. The model which we present is an ensemble of various neural architectures and gradient boosted trees, …...

26089

AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractIn this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human’s two-step procedure of sentence understanding and it can effectively represent …...

26102

DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractIn this paper, we propose a regression system to infer the emotion intensity of a tweet. We develop a multi-aspect feature learning mechanism to capture the most discriminative semantic features …...

26077

deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThis paper describes our system implementation for subtask V-oc of SemEval-2018 Task 1: affect in tweets. We use multi-task learning method to learn shared representation, then learn the features for …...

26087

EMA at SemEval-2018 Task 1: Emotion Mining for Arabic

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractWhile significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic. In fact, businesses are …...

26085

KDE-AFFECT at SemEval-2018 Task 1: Estimation of Affects in Tweet by Using Convolutional Neural Network for n-gram

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThis paper describes our approach to SemEval-2018 Task1: Estimation of Affects in Tweet for 1a and 2a. Our team KDE-AFFECT employs several methods including one-dimensional Convolutional Neural Network for n-grams, …...

26099

RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractWe present our methods and results for affect analysis in Twitter developed as a part of SemEval-2018 Task 1, where the sub-tasks involve predicting the intensity of emotion, the intensity …...

26068

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

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractEmotion 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 describe …...

26104

SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThe system developed by the SSN MLRG1 team for Semeval-2018 task 1 on affect in tweets uses rule based feature selection and one-hot encoding to generate the input feature vector. …...

26073

THU_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractTraditional sentiment analysis approaches mainly focus on classifying the sentiment polarities or emotion categories of texts. However, they can’t exploit the sentiment intensity information. Therefore, the SemEval-2018 Task 1 is …...

26094

UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThe present study describes our submission to SemEval 2018 Task 1: Affect in Tweets. Our Spanish-only approach aimed to demonstrate that it is beneficial to automatically generate additional training data …...

26082

Affect inTweets: A Transfer Learning Approach

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractPeople convey sentiments and emotions through language. To understand these affectual states is an essential step towards understanding natural language. In this paper, we propose a transfer-learning based approach to …...

15482