pwc-archive/papers-with-abstracts huggingface dataset:
Amrita\_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets
Authors: Nidhin A Unnithan, Shalini K., Barathi Ganesh H. B., An Kumar M, Soman K. P.
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning, Classification, Emotion Classification, General Classification, regression, Sentiment Analysis

In this paper we did an analysis of {``}Affects in Tweets{''} which was one of the task conducted by semeval 2018. Task was to build a model which is able to do regression and classification of different emotions from the given tweets data set. We developed a base model for all the subtasks using distributed representation (Doc2Vec) and applied machine learning techniques for classification and regression. Distributed representation is an unsupervised algorithm which is capable of learning fixed length feature representation from variable length texts. Machine learning techniques used for regression is {'}Linear Regression{'} while {'}Random Forest Tree{'} is used for classification purpose. Empirical results obtained for all the subtasks by our model are shown in this paper.
SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection
Authors: Angel Deborah S, Rajalakshmi S, S Milton Rajendram, Mirnalinee T T
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, feature selection, General Classification, Ordinal Classification, regression, Sentiment Analysis

The 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. Multilayer Perceptron was used to build the model for emotion intensity ordinal classification, sentiment analysis ordinal classification and emotion classfication subtasks. Support Vector Machine was used to build the model for emotion intensity regression and sentiment intensity regression subtasks.
CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets
Authors: Naveen J R, Barathi Ganesh H. B., An Kumar M, Soman K. P
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: General Classification, Sentiment Analysis, text-classification, Text Classification

This paper discusses on task 1, {``}Affect in Tweets{''} sharedtask, conducted in SemEval-2018. This task comprises of various subtasks, which required participants to analyse over different emotions and sentiments based on the provided tweet data and also measure the intensity of these emotions for subsequent subtasks. Our approach in these task was to come up with a model on count based representation and use machine learning techniques for regression and classification related tasks. In this work, we use a simple bag of words technique for supervised text classification model as to compare, that even with some advance distributed representation models we can still achieve significant accuracy. Further, fine tuning on various parameters for the bag of word, representation model we acquired better scores over various other baseline models (Vinayan et al.) participated in the sharedtask.
TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection
Authors: Anon George, Barathi Ganesh H. B., An Kumar M, Soman K. P
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Dimensionality Reduction, General Classification, text-classification, Text Classification

Emotions are a way of expressing human sentiments. In the modern era, social media is a platform where we convey our emotions. These emotions can be joy, anger, sadness and fear. Understanding the emotions from the written sentences is an interesting part in knowing about the writer. In the amount of digital language shared through social media, a considerable amount of data reflects the sentiment or emotion towards some product, person and organization. Since these texts are from users with diverse social aspects, these texts can be used to enrich the application related to the business intelligence. More than the sentiment, identification of intensity of the sentiment will enrich the performance of the end application. In this paper we experimented the intensity prediction as a text classification problem that evaluates the distributed representation text using aggregated sum and dimensionality reduction of the glove vectors of the words present in the respective texts .
IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction
Authors: Bhaskar Kotakonda, Prashanth Gowda, Brejesh lall
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Recognition, POS, Sentiment Analysis

This paper discusses the experiments performed for predicting the emotion intensity in tweets using a generalized supervised learning approach. We extract 3 kind of features from each of the tweets - one denoting the sentiment and emotion metrics obtained from different sentiment lexicons, one denoting the semantic representation of the word using dense representations like Glove, Word2vec and finally the syntactic information through POS N-grams, Word clusters, etc. We provide a comparative analysis of the significance of each of these features individually and in combination tested over standard regressors avaliable in scikit-learn. We apply an ensemble of these models to choose the best combination over cross validation.
Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection
Authors: Pan Du, Jian-Yun Nie
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, Emotion Recognition, General Classification, Product Recommendation, Sentiment Analysis

This paper describes MuTuX, our system that is designed for task 1-5a, emotion classification analysis of tweets on SemEval2018. The system aims at exploring the potential of context information of terms for emotion analysis. A Recurrent Neural Network is adopted to capture the context information of terms in tweets. Only term features and the sequential relations are used in our system. The results submitted ranks 16th out of 35 systems on the task of emotion detection in English-language tweets.
TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep Learning
Authors: Malak Abdullah, Samira Shaikh
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis

Task 1 in the International Workshop SemEval 2018, Affect in Tweets, introduces five subtasks (El-reg, El-oc, V-reg, V-oc, and E-c) to detect the intensity of emotions in English, Arabic, and Spanish tweets. This paper describes TeamUNCC{'}s system to detect emotions in English and Arabic tweets. Our approach is novel in that we present the same architecture for all the five subtasks in both English and Arabic. The main input to the system is a combination of word2vec and doc2vec embeddings and a set of psycholinguistic features (e.g. from AffectTweets Weka-package). We apply a fully connected neural network architecture and obtain performance results that show substantial improvements in Spearman correlation scores over the baseline models provided by Task 1 organizers, (ranging from 0.03 to 0.23). TeamUNCC{'}s system ranks third in subtask El-oc and fourth in other subtasks for Arabic tweets.
RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning
Authors: Venkatesh Elango, Karan Uppal
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Decoder, Sentiment Analysis

We 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 of sentiment, and valence for tweets. For modeling, though we use a traditional LSTM network, we combine our model with several state-of-the-art techniques to improve its performance in a low-resource setting. For example, we use an encoder-decoder network to initialize the LSTM weights. Without any task specific optimization we achieve competitive results (macro-average Pearson correlation coefficient 0.696) in the El-reg task. In this paper, we describe our development strategy in detail along with an exposition of our results.
ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets
Authors: El Moatez Billah Nagoudi
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Arabic Sentiment Analysis, Negation, regression, Sentiment Analysis

This article describes our proposed Arabic Sentiment Analysis system named ARB-SEN. This system is designed for the International Workshop on Semantic Evaluation 2018 (SemEval-2018), Task1: Affect in Tweets. ARB-SEN proposes two supervised models to estimate the sentiment intensity in Arabic tweets. Both models use a set of features including sentiment lexicon, negation, word embedding and emotion symbols features. Our system combines these features to assist the sentiment analysis task. ARB-SEN system achieves a correlation score of 0.720, ranking 6th among all participants in the valence intensity regression (V-reg) for the Arabic sub-task organized within the SemEval 2018 evaluation campaign.
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis
Authors: Grace Gee, Eugene Wang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, Emotion Recognition, General Classification, Sentiment Analysis, Transfer Learning

In this paper, we describe the first attempt to perform transfer learning from sentiment to emotions. Our system employs Long Short-Term Memory (LSTM) networks, including bidirectional LSTM (biLSTM) and LSTM with attention mechanism. We perform transfer learning by first pre-training the LSTM networks on sentiment data before concatenating the penultimate layers of these networks into a single vector as input to new dense layers. For the E-c subtask, we utilize a novel approach to train models for correlated emotion classes. Our system performs 4/48, 3/39, 8/38, 4/37, 4/35 on all English subtasks EI-reg, EI-oc, V-reg, V-oc, E-c of SemEval 2018 Task 1: Affect in Tweets.
UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models
Authors: Abhishek Avinash Narwekar, Roxana Girju
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: General Classification, Ordinal Classification, Sentiment Analysis

Our submission to the SemEval-2018 Task1: Affect in Tweets shared task competition is a supervised learning model relying on standard lexicon features coupled with word embedding features. We used an ensemble of diverse models, including random forests, gradient boosted trees, and linear models, corrected for training-development set mismatch. We submitted the system{'}s output for subtasks 1 (emotion intensity prediction), 2 (emotion ordinal classification), 3 (valence intensity regression) and 4 (valence ordinal classification), for English tweets. We placed 25th, 19th, 24th and 15th in the four subtasks respectively. The baseline considered was an SVM (Support Vector Machines) model with linear kernel on the lexicon and embedding based features. Our system{'}s final performance measured in Pearson correlation scores outperformed the baseline by a margin of 2.2{\%} to 14.6{\%} across all tasks.
KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets
Authors: Thomas Nyegaard-Signori, Casper Veistrup Helms, Johannes Bjerva, Isabelle Augenstein
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, General Classification, Multi-Task Learning, regression

We take a multi-task learning approach to the shared Task 1 at SemEval-2018. The general idea concerning the model structure is to use as little external data as possible in order to preserve the task relatedness and reduce complexity. We employ multi-task learning with hard parameter sharing to exploit the relatedness between sub-tasks. As a base model, we use a standard recurrent neural network for both the classification and regression subtasks. Our system ranks 32nd out of 48 participants with a Pearson score of 0.557 in the first subtask, and 20th out of 35 in the fifth subtask with an accuracy score of 0.464.
EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM
Authors: Man Liu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning, Feature Engineering, regression, Task 2

This paper describes our system used in the English Emoji Prediction Task 2 at the SemEval-2018. Our system is based on two supervised machine learning algorithms: Gradient Boosting Regression Tree Method (GBM) and Bidirectional Long Short-term Memory Network (BLSTM). Besides the common features, we extract various lexicon and syntactic features from external resources. After comparing the results of two algorithms, GBM is chosen for the final evaluation.
UMDuluth-CS8761 at SemEval-2018 Task 2: Emojis: Too many Choices?
Authors: Jonathan Beaulieu, Dennis Asamoah Owusu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Task 2

In this paper, we present our system for assigning an emoji to a tweet based on the text. Each tweet was originally posted with an emoji which the task providers removed. Our task was to decide out of 20 emojis, which originally came with the tweet. Two datasets were provided - one in English and the other in Spanish. We treated the task as a standard classification task with the emojis as our classes and the tweets as our documents. Our best performing system used a Bag of Words model with a Linear Support Vector Machine as its{'} classifier. We achieved a macro F1 score of 32.73{\%} for the English data and 17.98{\%} for the Spanish data.
The Dabblers at SemEval-2018 Task 2: Multilingual Emoji Prediction
Authors: Larisa Alexa, Alina Loren{\textcommabelow{t}}, Daniela G{\^\i}fu, Tr, Diana ab{\u{a}}{\textcommabelow{t}}
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning, Relation, Task 2

The {``}Multilingual Emoji Prediction{''} task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network).
THU\_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction
Authors: Chuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Junxin Liu, Yongfeng Huang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: POS, Sentiment Analysis, Task 2

Emojis are widely used by social media and social network users when posting their messages. It is important to study the relationships between messages and emojis. Thus, in SemEval-2018 Task 2 an interesting and challenging task is proposed, i.e., predicting which emojis are evoked by text-based tweets. We propose a residual CNN-LSTM with attention (\textbf{RCLA}) model for this task. Our model combines CNN and LSTM layers to capture both local and long-range contextual information for tweet representation. In addition, attention mechanism is used to select important components. Besides, residual connection is applied to CNN layers to facilitate the training of neural networks. We also incorporated additional features such as POS tags and sentiment features extracted from lexicons. Our model achieved 30.25{\%} macro-averaged F-score in the first subtask (i.e., emoji prediction in English), ranking 7th out of 48 participants.
\#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets
Authors: Alison Ribeiro, N{\'a}dia Silva
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: General Classification, Information Retrieval, regression, Sentiment Analysis, Task 2, Word Embeddings

In this paper, we describe a methodology to predict emoji in tweets. Our approach is based on the classic bag-of-words model in conjunction with word embeddings. The used classification algorithm was Logistic Regression. This architecture was used and evaluated in the context of the SemEval 2018 challenge (task 2, subtask 1).
EICA Team at SemEval-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji Prediction
Authors: Yufei Xie, Qingqing Song
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Information Retrieval, Prediction, Sentiment Analysis, Task 2, Word Embeddings

The advent of social media has brought along a novel way of communication where meaning is composed by combining short text messages and visual enhancements, the so-called emojis. We describe our system for participating in SemEval-2018 Task 2 on Multilingual Emoji Prediction. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important types turned out to be the metadata feature. In subtask 1: Emoji Prediction in English, our primary submission obtain a MAP of 16.45, Precision of 31.557, Recall of 16.771 and Accuracy of 30.992.
EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words
Authors: Shiyun Chen, Maoquan Wang, Liang He
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Task 2

This paper presents our single model to Subtask 1 of SemEval 2018 Task 2: Emoji Prediction in English. In order to predict the emoji that may be contained in a tweet, the basic model we use is an attention-based recurrent neural network which has achieved satisfactory performs in Natural Language processing. Considering the text comes from social media, it contains many discrepant abbreviations and online terms, we also combine word-level and character-level word vector embedding to better handling the words not appear in the vocabulary. Our single model1 achieved 29.50{\%} Macro F-score in test data and ranks 9th among 48 teams.
Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction
Authors: Jing Chen, Dechuan Yang, Xilian Li, Wei Chen, Tengjiao Wang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, General Classification, Semantic Textual Similarity, Sentiment Analysis, Task 2, text similarity, Word Embeddings

This paper describes our participation in SemEval 2018 Task 2: Multilingual Emoji Prediction, in which participants are asked to predict a tweet{'}s most associated emoji from 20 emojis. Instead of regarding it as a 20-class classification problem we regard it as a text similarity problem. We propose a vector similarity based approach for this task. First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji{'}s embedding is evaluated. The most similar emoji is chosen as the predicted label. Experimental results show that our approach performs comparably with the classification approach and shows its advantage in classifying emojis with similar semantic meaning.
ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Features and Neural Networks Methods to Address Twitter Emoji Prediction Task
Authors: Xingwu Lu, Xin Mao, Man Lan, Yuanbin Wu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Deep Learning, Prediction, Task 2

This paper describes our submissions to Task 2 in SemEval 2018, i.e., Multilingual Emoji Prediction. We first investigate several traditional Natural Language Processing (NLP) features, and then design several deep learning models. For subtask 1: Emoji Prediction in English, we combine two different methods to represent tweet, i.e., supervised model using traditional features and deep learning model. For subtask 2: Emoji Prediction in Spanish, we only use deep learning model.
Hatching Chick at SemEval-2018 Task 2: Multilingual Emoji Prediction
Authors: Jo{\"e}l Coster, Reinder Gerard van Dalen, Nathalie Adri{\"e}nne Jacqueline Stierman
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Task 2

As part of a SemEval 2018 shared task an attempt was made to build a system capable of predicting the occurence of a language{'}s most frequently used emoji in Tweets. Specifically, models for English and Spanish data were created and trained on 500.000 and 100.000 tweets respectively. In order to create these models, first a logistic regressor, a sequential LSTM, a random forest regressor and a SVM were tested. The latter was found to perform best and therefore optimized individually for both languages. During developmet f1-scores of 61 and 82 were obtained for English and Spanish data respectively, in comparison, f1-scores on the official evaluation data were 21 and 18. The significant decrease in performance during evaluation might be explained by overfitting during development and might therefore have partially be prevented by using cross-validation. Over all, emoji which occur in a very specific context such as a Christmas tree were found to be most predictable.
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption
Authors: Liyuan Zhou, Qiongkai Xu, Hanna Suominen, Tom Gedeon
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, General Classification, Sentiment Analysis, Task 2, text-classification, Text Classification, Transfer Learning, Twitter Sentiment Analysis

This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media {---} more precisely, Twitter {---} with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification problem explores the idea of transfer learning for adapting the classifier based on users{'} tweeting history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro-averaged F1. Thus, our paper provides evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances.
PickleTeam! at SemEval-2018 Task 2: English and Spanish Emoji Prediction from Tweets
Authors: Daphne Groot, R{\'e}mon Kruizinga, Hennie Veldthuis, Simon de Wit, Hessel Haagsma
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Medical Diagnosis, Prediction, Task 2

We present a system for emoji prediction on English and Spanish tweets, prepared for the SemEval-2018 task on Multilingual Emoji Prediction. We compared the performance of an SVM, LSTM and an ensemble of these two. We found the SVM performed best on our development set with an accuracy of 61.3{\%} for English and 83{\%} for Spanish. The features used for the SVM are lowercased word n-grams in the range of 1 to 20, tokenised by a TweetTokenizer and stripped of stop words. On the test set, our model achieved an accuracy of 34{\%} on English, with a slightly lower score of 29.7{\%} accuracy on Spanish.
YNU-HPCC at SemEval-2018 Task 2: Multi-ensemble Bi-GRU Model with Attention Mechanism for Multilingual Emoji Prediction
Authors: Nan Wang, Jin Wang, Xue-jie Zhang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis, Task 2

This paper describes our approach to SemEval-2018 Task 2, which aims to predict the most likely associated emoji, given a tweet in English or Spanish. We normalized text-based tweets during pre-processing, following which we utilized a bi-directional gated recurrent unit with an attention mechanism to build our base model. Multi-models with or without class weights were trained for the ensemble methods. We boosted models without class weights, and only strong boost classifiers were identified. In our system, not only was a boosting method used, but we also took advantage of the voting ensemble method to enhance our final system result. Our method demonstrated an obvious improvement of approximately 3{\%} of the macro F1 score in English and 2{\%} in Spanish.