pwc-archive/papers-with-abstracts huggingface dataset:
UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets
Authors: Pavel P{\v{r}}ib{\'a}{\v{n}}, Tom{\'a}{\v{s}} Hercig, Ladislav Lenc
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, General Classification, Ordinal Classification, POS, regression, Word Embeddings

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, Arabic, and Spanish. For the regression subtask we use the AffectiveTweets system with added features using various word embeddings, lexicons, and LDA. For the ordinal classification we additionally use our Brainy system with features using parse tree, POS tags, and morphological features. The most beneficial features apart from word and character n-grams include word embeddings, POS count and morphological features.
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis
Authors: Mario Graff, Mir, Sabino a-Jim{\'e}nez, Eric S. Tellez, Daniela Moctezuma
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Combinatorial Optimization, regression, Sentiment Analysis, Text Categorization

This paper describes our participation in Affective Tweets task for emotional intensity and sentiment intensity subtasks for English, Spanish, and Arabic languages. We used two approaches, μTC and EvoMSA. The first one is a generic text categorization and regression system; and the second one, a two-stage architecture for Sentiment Analysis. Both approaches are multilingual and domain independent.
Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach
Authors: Guillaume Daval-Frerot, Abdesselam Bouchekif, Anatole Moreau
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis, Transfer Learning

In this paper we present our system for detecting valence task. The major issue was to apply a state-of-the-art system despite the small dataset provided: the system would quickly overfit. The main idea of our proposal is to use transfer learning, which allows to avoid learning from scratch. Indeed, we start to train a first model to predict if a tweet is positive, negative or neutral. For this we use an external dataset which is larger and similar to the target dataset. Then, the pre-trained model is re-used as the starting point to train a new model that classifies a tweet into one of the seven various levels of sentiment intensity. Our system, trained using transfer learning, achieves 0.776 and 0.763 respectively for Pearson correlation coefficient and weighted quadratic kappa metrics on the subtask evaluation dataset.
KDE-AFFECT at SemEval-2018 Task 1: Estimation of Affects in Tweet by Using Convolutional Neural Network for n-gram
Authors: Masaki Aono, Shinnosuke Himeno
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis

This 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, together with word embedding and other preprocessing such as vocabulary unification and Emoji conversion into four emotional words.
RNN for Affects at SemEval-2018 Task 1: Formulating Affect Identification as a Binary Classification Problem
Authors: Aysu Ezen-Can, Ethem F. Can
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Action Recognition, Binary Classification, General Classification, Temporal Action Localization

Written communication lacks the multimodal features such as posture, gesture and gaze that make it easy to model affective states. Especially in social media such as Twitter, due to the space constraints, the sources of information that can be mined are even more limited due to character limitations. These limitations constitute a challenge for understanding short social media posts. In this paper, we present an approach that utilizes multiple binary classifiers that represent different affective categories to model Twitter posts (e.g., tweets). We train domain-independent recurrent neural network models without any outside information such as affect lexicons. We then use these domain independent binary ranking models to evaluate the applicability of such deep learning models on the affect identification task. This approach allows different model architectures and parameter settings for each affect category instead of building one single multi-label classifier. The contributions of this paper are two-folds: we show that modeling tweets with a small training set is possible with the use of RNNs and we also prove that formulating affect identification as a binary classification task is highly effective.
Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification
Authors: Hala Mulki, Chedi Bechikh Ali, Hatem Haddad, Ismail Babao{\u{g}}lu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, Emotion Classification, General Classification, Lemmatization, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentiment Analysis, Twitter Sentiment Analysis

In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 {``}Affect in Tweets{''}, subtask E-c {``}Detecting Emotions (multi-label classification){''}. A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets. To handle the multi-label classification problem via traditional classifiers, we employed the binary relevance transformation strategy while a TF-IDF scheme was used to generate the tweets{'} features. We investigated using single and combinations of several preprocessing tasks to further improve the performance. The results showed that specific combinations of preprocessing tasks could significantly improve the evaluation measures. This has been later emphasized by the official results as our system ranked 3rd for both Arabic and Spanish datasets and 14th for the English dataset.
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings
Authors: Dmitry Kravchenko, Lidia Pivovarova
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, Sentence, Word Embeddings

The paper describes our approach for SemEval-2018 Task 1: Affect Detection in Tweets. We perform experiments with manually compelled sentiment lexicons and word embeddings. We test their performance on twitter affect detection task to determine which features produce the most informative representation of a sentence. We demonstrate that general-purpose word embeddings produces more informative sentence representation than lexicon features. However, combining lexicon features with embeddings yields higher performance than embeddings alone.
EmoIntens Tracker at SemEval-2018 Task 1: Emotional Intensity Levels in \#Tweets
Authors: Ramona-Andreea Turcu, Amar, S ei, ra Maria, Iuliana-Alex Flescan-Lovin-Arseni, ra, Daniela Gifu, Tr, Diana abat
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning, Emotion Recognition, Sentiment Analysis

The â€žAffect 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.
uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network
Authors: Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Diana Inkpen, David Van Bruwaene
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: regression

We propose a novel attentive hybrid GRU-based network (SAHGN), which we used at SemEval-2018 Task 1: Affect in Tweets. Our network has two main characteristics, 1) has the ability to internally optimize its feature representation using attention mechanisms, and 2) provides a hybrid representation using a character level Convolutional Neural Network (CNN), as well as a self-attentive word-level encoder. The key advantage of our model is its ability to signify the relevant and important information that enables self-optimization. Results are reported on the valence intensity regression task.
THU\_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM
Authors: Chuhan Wu, Fangzhao Wu, Junxin Liu, Zhigang Yuan, Sixing Wu, Yongfeng Huang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: regression, Sentiment Analysis, Twitter Sentiment Analysis

Traditional 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 aimed to automatically determine the intensity of emotions or sentiment of tweets to mine fine-grained sentiment information. In order to address this task, we propose a system based on an attention CNN-LSTM model. In our model, LSTM is used to extract the long-term contextual information from texts. We apply attention techniques to selecting this information. A CNN layer with different size of kernels is used to extract local features. The dense layers take the pooled CNN feature maps and predict the intensity scores. Our system reaches average Pearson correlation score of 0.722 (ranked 12/48) in emotion intensity regression task, and 0.810 in valence regression task (ranked 15/38). It indicates that our system can be further extended.
CENTEMENT at SemEval-2018 Task 1: Classification of Tweets using Multiple Thresholds with Self-correction and Weighted Conditional Probabilities
Authors: Tariq Ahmad, Allan Ramsay, Hanady Ahmed
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Opinion Mining, Sentiment Analysis

In this paper we present our contribution to SemEval-2018, a classifier for classifying multi-label emotions of Arabic and English tweets. We attempted {``}Affect in Tweets{''}, specifically Task E-c: Detecting Emotions (multi-label classification). Our method is based on preprocessing the tweets and creating word vectors combined with a self correction step to remove noise. We also make use of emotion specific thresholds. The final submission was selected upon the best performance achieved, selected when using a range of thresholds. Our system was evaluated on the Arabic and English datasets provided for the task by the competition organisers, where it ranked 2nd for the Arabic dataset (out of 14 entries) and 12th for the English dataset (out of 35 entries).
Yuan at SemEval-2018 Task 1: Tweets Emotion Intensity Prediction using Ensemble Recurrent Neural Network
Authors: Min Wang, Xiaobing Zhou
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis

We perform the LSTM and BiLSTM model for the emotion intensity prediction. We only join the third subtask in Task 1:Affect in Tweets. Our system rank 6th among all the teams.
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets
Authors: Mostafa Abdou, Artur Kulmizev, Joan Gin{\'e}s i Ametll{\'e}
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, General Classification, Sentiment Analysis, Transfer Learning

In 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, and employs three different types of vectorial tweet representations. Furthermore, our system is language-independent and ranked first in 5 out of the 12 subtasks in which we participated, while achieving competitive results in the remaining ones. Comparatively remarkable performance is observed on both the Arabic and Spanish languages.
deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets
Authors: Zi-Yuan Gao, Chia-Ping Chen
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, General Classification, Multi-Task Learning, Sentiment Analysis

This 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 each task. There are five classification models in the proposed multi-task learning approach. These classification models are trained sequentially to learn different features for different classification tasks. In addition to the data released for SemEval-2018, we use datasets from previous SemEvals during system construction. Our Pearson correlation score is 0.638 on the official SemEval-2018 Task 1 test set.
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models
Authors: Huimin Xu, Man Lan, Yuanbin Wu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning, Emotion Classification, Feature Engineering, General Classification, Prediction, Sentiment Analysis

This paper describes our submissions to SemEval 2018 task 1. The task is affect intensity prediction in tweets, including five subtasks. We participated in all subtasks of English tweets. We extracted several traditional NLP, sentiment lexicon, emotion lexicon and domain specific features from tweets, adopted supervised machine learning algorithms to perform emotion intensity prediction.
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic
Authors: Gilbert Badaro, Obeida El Jundi, Alaa Khaddaj, Alaa Maarouf, Raslan Kain, Hazem Hajj, Wassim El-Hajj
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, Emotion Recognition, Opinion Mining, Recommendation Systems, Sentiment Analysis, Word Embeddings

While 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 interested in learning a fine-grained representation of how users are feeling towards their products or services. In this work, we describe the methods used by the team Emotion Mining in Arabic (EMA), as part of the SemEval-2018 Task 1 for Affect Mining for Arabic tweets. EMA participated in all 5 subtasks. For the five tasks, several preprocessing steps were evaluated and eventually the best system included diacritics removal, elongation adjustment, replacement of emojis by the corresponding Arabic word, character normalization and light stemming. Moreover, several features were evaluated along with different classification and regression techniques. For the 5 subtasks, word embeddings feature turned out to perform best along with Ensemble technique. EMA achieved the 1st place in subtask 5, and 3rd place in subtasks 1 and 3.
CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons
Authors: Raj Kumar Gupta, Yinping Yang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, Emotion Recognition, Sentiment Analysis

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.
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and \#hashtags
Authors: Ji Ho Park, Peng Xu, Pascale Fung
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning, Emotion Classification, General Classification, regression, Sentence, Sentiment Analysis

This paper describes our system that has been submitted to SemEval-2018 Task 1: Affect in Tweets (AIT) to solve five subtasks. We focus on modeling both sentence and word level representations of emotion inside texts through large distantly labeled corpora with emojis and hashtags. We transfer the emotional knowledge by exploiting neural network models as feature extractors and use these representations for traditional machine learning models such as support vector regression (SVR) and logistic regression to solve the competition tasks. Our system is placed among the Top3 for all subtasks we participated.
YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in Tweets
Authors: You Zhang, Jin Wang, Xue-jie Zhang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, Domain Adaptation, Ensemble Learning, General Classification, Multi-Label Classification, Ordinal Classification, regression, Sentiment Analysis

We implemented the sentiment system in all five subtasks for English and Spanish. All subtasks involve emotion or sentiment intensity prediction (regression and ordinal classification) and emotions determining (multi-labels classification). The useful BiLSTM (Bidirectional Long-Short Term Memory) model with attention mechanism was mainly applied for our system. We use BiLSTM in order to get word information extracted from both directions. The attention mechanism was used to find the contribution of each word for improving the scores. Furthermore, based on BiLSTMATT (BiLSTM with attention mechanism) a few deep-learning algorithms were employed for different subtasks. For regression and ordinal classification tasks we used domain adaptation and ensemble learning methods to leverage base model. While a single base model was used for multi-labels task.
ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets
Authors: Meng Li, Zhenyuan Dong, Zhihao Fan, Kongming Meng, Jinghua Cao, Guanqi Ding, Yu-Han Liu, Jiawei Shan, Binyang Li
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, Emotion Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentiment Analysis, Sentiment Classification

This paper presents a UIR-Miner system for emotion and sentiment analysis evaluation in Twitter in SemEval 2018. Our system consists of three main modules: preprocessing module, stacking module to solve the intensity prediction of emotion and sentiment, LSTM network module to solve multi-label classification, and the hierarchical attention network module for solving emotion and sentiment classification problem. According to the metrics of SemEval 2018, our system gets the final scores of 0.636, 0.531, 0.731, 0.708, and 0.408 on 5 subtasks, respectively.
TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture
Authors: Hardik Meisheri, Lipika Dey
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis

This paper presents system description of our submission to the SemEval-2018 task-1: Affect in tweets for the English language. We combine three different features generated using deep learning models and traditional methods in support vector machines to create a unified ensemble system. A robust representation of a tweet is learned using a multi-attention based architecture which uses a mixture of different pre-trained embeddings. In addition to this analysis of different features is also presented. Our system ranked 2nd, 5th, and 7th in different subtasks among 75 teams.
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction
Authors: Youngmin Kim, Hyunju Lee
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: General Classification, regression, Sentiment Analysis, Sentiment Classification, Transfer Learning, Word Embeddings

This paper describes a system attended in the SemEval-2018 Task 1 {``}Affect in tweets{''} that predicts emotional intensities. We use Group LSTM with an attention model and transfer learning with sentiment classification data as a source data (SemEval 2017 Task 4a). A transfer model structure consists of a source domain and a target domain. Additionally, we try a new dropout that is applied to LSTMs in the Group LSTM. Our system ranked 8th at the subtask 1a (emotion intensity regression). We also show various results with different architectures in the source, target and transfer models.
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning
Authors: Habibeh Naderi, Behrouz Haji Soleimani, Saif Mohammad, Svetlana Kiritchenko, Stan Matwin
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Feature Engineering, Representation Learning, Sentiment Analysis, Word Embeddings

In 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 of a tweet as well as the emotion information conveyed by each word in it. We combine six types of feature groups: (1) a tweet representation learned by an LSTM deep neural network on the training data, (2) a tweet representation learned by an LSTM network on a large corpus of tweets that contain emotion words (a distant supervision corpus), (3) word embeddings trained on the distant supervision corpus and averaged over all words in a tweet, (4) word and character n-grams, (5) features derived from various sentiment and emotion lexicons, and (6) other hand-crafted features. As part of the word embedding training, we also learn the distributed representations of multi-word expressions (MWEs) and negated forms of words. An SVR regressor is then trained over the full set of features. We evaluate the effectiveness of our ensemble feature sets on the SemEval-2018 Task 1 datasets and achieve a Pearson correlation of 72{\%} on the task of tweet emotion intensity prediction.
Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets
Authors: Zewen Chi, He-Yan Huang, Jiangui Chen, Hao Wu, Ran Wei
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentence Classification, Sentiment Analysis

This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets. The term affect refers to emotion-related categories such as anger, fear, etc. Intensity of emo-tions need to be quantified into a real valued score in [0, 1]. We propose an en-semble system including four different deep learning methods which are CNN, Bidirectional LSTM (BLSTM), LSTM-CNN and a CNN-based Attention model (CA). Our system gets an average Pearson correlation score of 0.682 in the subtask EI-reg and an average Pearson correlation score of 0.784 in subtask V-reg, which ranks 17th among 48 systems in EI-reg and 19th among 38 systems in V-reg.