pwc-archive/papers-with-abstracts huggingface dataset:
NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture
Authors: Nikolay Karpov
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Opinion Mining, Sentiment Analysis, Text Classification, Word Sense Disambiguation

In many areas, such as social science, politics or market research, people need to deal with dataset shifting over time. Distribution drift phenomenon usually appears in the field of sentiment analysis, when proportions of instances are changing over time. In this case, the task is to correctly estimate proportions of each sentiment expressed in the set of documents (quantification task). Basically, our study was aimed to analyze the effectiveness of a mixture of quantification technique with one of deep learning architecture. All the techniques are evaluated using the SemEval-2017 Task4 dataset and source code, mentioned in this paper and available online in the Python programming language. The results of an application of the quantification techniques are discussed.
MI\&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification
Authors: Jingjing Zhao, Yan Yang, Bing Xu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentence, Sentiment Analysis, Sentiment Classification, Twitter Sentiment Analysis

A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented. To solve the problem of word2vec training word vector slowly, a method of training word vector by integrating word2vec and Convolutional Neural Network (CNN) is proposed. This training method not only improves the training speed of word2vec, but also makes the word vector more effective for the target task. Furthermore, the word2vec adopts a full connection between the input layer and the projection layer of the Continuous Bag-of-Words (CBOW) for acquiring the semantic information of the original sentence.
SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features
Authors: Mohammed Jabreel, Antonio Moreno
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentiment Analysis, Twitter Sentiment Analysis

This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. Our system ranks 2nd among 8 systems in the Arabic language tweets and ranks 8th among 38 systems in the English-language tweets.
DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis
Authors: Symeon Symeonidis, Dimitrios Effrosynidis, John Kordonis, Avi Arampatzis
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: BIG-bench Machine Learning, General Classification, Information Retrieval, Sentiment Analysis, Sentiment Classification, Twitter Sentiment Analysis

This report describes our participation to SemEval-2017 Task 4: Sentiment Analysis in Twitter, specifically in subtasks A, B, and C. The approach for text sentiment classification is based on a Majority Vote scheme and combined supervised machine learning methods with classical linguistic resources, including bag-of-words and sentiment lexicon features.
SSN\_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier
Authors: Angel Deborah S, S Milton Rajendram, T T Mirnalinee
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentiment Analysis

The SSN MLRG1 team for Semeval-2017 task 4 has applied Gaussian Process, with bag of words feature vectors and fixed rule multi-kernel learning, for sentiment analysis of tweets. Since tweets on the same topic, made at different times, may exhibit different emotions, their properties such as smoothness and periodicity also vary with time. Our experiments show that, compared to single kernel, multiple kernels are effective in learning the simultaneous presence of multiple properties.
YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter
Authors: Ming Wang, Biao Chu, Qingxun Liu, Xiaobing Zhou
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

Sentiment analysis is one of the central issues in Natural Language Processing and has become more and more important in many fields. Typical sentiment analysis classifies the sentiment of sentences into several discrete classes (e.g.,positive or negative). In this paper we describe our deep learning system(combining GRU and SVM) to solve both two-, three- and five-tweet polarity classifications. We first trained a gated recurrent neural network using pre-trained word embeddings, then we extracted features from GRU layer and input these features into support vector machine to fulfill both the classification and quantification subtasks. The proposed approach achieved 37th, 19th, and 14rd places in subtasks A, B and C, respectively.
LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification
Authors: Amal Htait, S{\'e}bastien Fournier, Patrice Bellot
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, General Classification, Semantic Textual Similarity, Sentiment Analysis

We present, in this paper, our contribution in SemEval2017 task 4 : {``}Sentiment Analysis in Twitter{''}, subtask A: {``}Message Polarity Classification{''}, for English and Arabic languages. Our system is based on a list of sentiment seed words adapted for tweets. The sentiment relations between seed words and other terms are captured by cosine similarity between the word embedding representations (word2vec). These seed words are extracted from datasets of annotated tweets available online. Our tests, using these seed words, show significant improvement in results compared to the use of Turney and Littman{'}s (2003) seed words, on polarity classification of tweet messages.
ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis using Deep Learning
Authors: Jos{\'e}-{\'A}ngel Gonz{\'a}lez, Ferran Pla, Llu{\'\i}s-F. Hurtado
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Opinion Mining, Sentiment Analysis, Word Embeddings

This paper describes the participation of ELiRF-UPV team at task 4 of SemEval2017. Our approach is based on the use of convolutional and recurrent neural networks and the combination of general and specific word embeddings with polarity lexicons. We participated in all of the proposed subtasks both for English and Arabic languages using the same system with small variations.
XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter
Authors: Yazhou Hao, Yangyang Lan, Yufei Li, Chen Li
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Semantic Parsing, Sentiment Analysis, Sentiment Classification, Speech Recognition

This paper describes the XJSA System submission from XJTU. Our system was created for SemEval2017 Task 4 {--} subtask A which is very popular and fundamental. The system is based on convolutional neural network and word embedding. We used two pre-trained word vectors and adopt a dynamic strategy for k-max pooling.
Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention
Authors: Joosung Yoon, Kigon Lyu, Hyeoncheol Kim
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentiment Analysis, Word Embeddings

We propose a sentiment analyzer for the prediction of document-level sentiments of English micro-blog messages from Twitter. The proposed method is based on lexicon integrated convolutional neural networks with attention (LCA). Its performance was evaluated using the datasets provided by SemEval competition (Task 4). The proposed sentiment analyzer obtained an average F1 of 55.2{\%}, an average recall of 58.9{\%} and an accuracy of 61.4{\%}.
EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification
Authors: Maoquan Wang, Shiyun Chen, Yufei Xie, Lu Zhao
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, Feature Engineering, General Classification, Sentence, Sentence Classification, Sentiment Analysis, Sentiment Classification, Twitter Sentiment Analysis

This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentiment polarity, and (2) sentiment quantification, that is, estimating the sentiment distributions of a set of given tweets. We build a convolutional sentence classification system for the task of SAT. Official results show that the experimental results of our system are comparative.
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts
Authors: Quanzhi Li, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang, Sameena Shah
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, General Classification, Negation, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification, Word Embeddings

This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter. Topic-based (target-dependent) sentiment analysis has become attractive and been used in some applications recently, but it is still a challenging research task. In our approach, we take the left and right context of a target into consideration when generating polarity classification features. We use two types of word embeddings in our classifiers: the general word embeddings learned from 200 million tweets, and sentiment-specific word embeddings learned from 10 million tweets using distance supervision. We also incorporate a text feature model in our algorithm. This model produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. We participated in four subtasks (B, C, D {\&} E for English), all of which are about topic-based message polarity classification. Our team is ranked {\#}6 in subtask B, {\#}3 by MAEu and {\#}9 by MAEm in subtask C, {\#}3 using RAE and {\#}6 using KLD in subtask D, and {\#}3 in subtask E.
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis
Authors: Christos Baziotis, Nikos Pelekis, Christos Doulkeridis
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Feature Engineering, Sentiment Analysis, Word Embeddings

In this paper we present two deep-learning systems that competed at SemEval-2017 Task 4 {``}Sentiment Analysis in Twitter{''}. We participated in all subtasks for English tweets, involving message-level and topic-based sentiment polarity classification and quantification. We use Long Short-Term Memory (LSTM) networks augmented with two kinds of attention mechanisms, on top of word embeddings pre-trained on a big collection of Twitter messages. Also, we present a text processing tool suitable for social network messages, which performs tokenization, word normalization, segmentation and spell correction. Moreover, our approach uses no hand-crafted features or sentiment lexicons. We ranked 1st (tie) in Subtask A, and achieved very competitive results in the rest of the Subtasks. Both the word embeddings and our text processing tool are available to the research community.
TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification
Authors: Georgios Balikas
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Opinion Mining, regression, Sentiment Analysis, Sentiment Classification, Twitter Sentiment Analysis, Word Embeddings

The paper describes the participation of the team {``}TwiSE{''} in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled {``}Sentiment Analysis in Twitter{''} for which I implemented systems for five-point tweet classification (Subtask C) and five-point tweet quantification (Subtask E) for English tweets. In the feature extraction steps the systems rely on the vector space model, morpho-syntactic analysis of the tweets and several sentiment lexicons. The classification step of Subtask C uses a Logistic Regression trained with the one-versus-rest approach. Another instance of Logistic Regression combined with the classify-and-count approach is trained for the quantification task of Subtask E. In the official leaderboard the system is ranked \textit{5/15} in Subtask C and \textit{2/12} in Subtask E.
LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification
Authors: Mickael Rouvier
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentence Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

This paper describes the system developed at LIA for the SemEval-2017 evaluation campaign. The goal of Task 4.A was to identify sentiment polarity in tweets. The system is an ensemble of Deep Neural Network (DNN) models: Convolutional Neural Network (CNN) and Recurrent Neural Network Long Short-Term Memory (RNN-LSTM). We initialize the input representation of DNN with different sets of embeddings trained on large datasets. The ensemble of DNNs are combined using a score-level fusion approach. The system ranked 2nd at SemEval-2017 and obtained an average recall of 67.6{\%}.
TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision
Authors: Simon M{\"u}ller, Tobias Huonder, Jan Deriu, Mark Cieliebak
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

In this paper, we propose a classifier for predicting topic-specific sentiments of English Twitter messages. Our method is based on a 2-layer CNN.With a distant supervised phase we leverage a large amount of weakly-labelled training data. Our system was evaluated on the data provided by the SemEval-2017 competition in the Topic-Based Message Polarity Classification subtask, where it ranked 4th place.
INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis
Authors: Mir, Sabino a-Jim{\'e}nez, Mario Graff, Eric Sadit Tellez, Daniela Moctezuma
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, Combinatorial Optimization, General Classification, Sentiment Analysis, Twitter Sentiment Analysis

This paper describes the system used in SemEval-2017 Task 4 (Subtask A): Message Polarity Classification for both English and Arabic languages. Our proposed system is an ensemble of two layers, the first one uses our generic framework for multilingual polarity classification (B4MSA) and the second layer combines all the decision function values predicted by B4MSA systems using a non-linear function evolved using a Genetic Programming system, EvoDAG. With this approach, the best performances reached by our system were macro-recall 0.68 (English) and 0.477 (Arabic) which set us in sixth and fourth positions in the results table, respectively.
BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches
Authors: Deger Ayata, Murat Saraclar, Arzucan Ozgur
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, General Classification, Opinion Mining, Sentiment Analysis, Subjectivity Analysis, Word Embeddings

This paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have participated in Subtask A: Message Polarity Classification subtask and developed two systems. The first system uses word embeddings for feature representation and Support Vector Machine, Random Forest and Naive Bayes algorithms for classification of Twitter messages into negative, neutral and positive polarity. The second system is based on Long Short Term Memory Recurrent Neural Networks and uses word indexes as sequence of inputs for feature representation.
TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in Twitter
Authors: David Lozi{\'c}, Doria {\v{S}}ari{\'c}, Ivan Toki{\'c}, Zoran Medi{\'c}, Jan {\v{S}}najder
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentiment Analysis, Word Embeddings

This paper describes the system we submitted to SemEval-2017 Task 4 (Sentiment Analysis in Twitter), specifically subtasks A, B, and D. Our main focus was topic-based message polarity classification on a two-point scale (subtask B). The system we submitted uses a Support Vector Machine classifier with rich set of features, ranging from standard to more creative, task-specific features, including a series of rating-based features as well as features that account for sentimental reminiscence of past topics and deceased famous people. Our system ranked 14th out of 39 submissions in subtask A, 5th out of 24 submissions in subtask B, and 3rd out of 16 submissions in subtask D.
YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classification
Authors: Haowei Zhang, Jin Wang, Jixian Zhang, Xue-jie Zhang
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentiment Analysis, Sentiment Classification, Text Classification

In this paper, we propose a multi-channel convolutional neural network-long short-term memory (CNN-LSTM) model that consists of two parts: multi-channel CNN and LSTM to analyze the sentiments of short English messages from Twitter. Un-like a conventional CNN, the proposed model applies a multi-channel strategy that uses several filters of different length to extract active local n-gram features in different scales. This information is then sequentially composed using LSTM. By combining both CNN and LSTM, we can consider both local information within tweets and long-distance dependency across tweets in the classification process. Officially released results show that our system outperforms the baseline algo-rithm.
TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis
Authors: Amit Ajit Deshmane, Jasper Friedrichs
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentiment Analysis, text-classification, Text Classification, Twitter Sentiment Analysis

This paper describes the submission of team TSA-INF to SemEval-2017 Task 4 Subtask A. The submitted system is an ensemble of three varying deep learning architectures for sentiment analysis. The core of the architecture is a convolutional neural network that performs well on text classification as is. The second subsystem is a gated recurrent neural network implementation. Additionally, the third system integrates opinion lexicons directly into a convolution neural network architecture. The resulting ensemble of the three architectures achieved a top ten ranking with a macro-averaged recall of 64.3{\%}. Additional results comparing variations of the submitted system are not conclusive enough to determine a best architecture, but serve as a benchmark for further implementations.
UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter
Authors: Jos{\'e} Abreu, Iv{\'a}n Castro, Claudia Mart{\'\i}nez, Sebasti{\'a}n Oliva, Yoan Guti{\'e}rrez
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentiment Analysis

This paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can contribute to this task, i.e. co-occurrences of WordNet senses in the tweet, and the polarity. Furthermore, we evaluated the effect of discarding a large set of features based on char-grams reported in preceding works. Based on these elements, we developed a SVM system, which exploring SentiWordNet as a polarity lexicon. It achieves an $F_1=0.624$of average. Among 39 submissions to this task, we ranked 10th.
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification
Authors: Yunxiao Zhou, Man Lan, Yuanbin Wu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: BIG-bench Machine Learning, Feature Engineering, General Classification, Lemmatization, Sentiment Analysis, Word Embeddings

This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average.
SSN\_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model
Authors: Angel Deborah S, S Milton Rajendram, T T Mirnalinee
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Decision Making, Sentiment Analysis

The system developed by the SSN{\_}MLRG1 team for Semeval-2017 task 5 on fine-grained sentiment analysis uses Multiple Kernel Gaussian Process for identifying the optimistic and pessimistic sentiments associated with companies and stocks. Since the comments made at different times about the same companies and stocks may display different emotions, their properties such as smoothness and periodicity may vary. Our experiments show that while single kernel Gaussian Process can learn certain properties well, Multiple Kernel Gaussian Process are effective in learning the presence of different properties simultaneously.
IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Authors: Zarmeen Nasim
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: regression, Sentiment Analysis

This paper presents the details of our system IBA-Sys that participated in SemEval Task: Fine-grained sentiment analysis on Financial Microblogs and News. Our system participated in both tracks. For microblogs track, a supervised learning approach was adopted and the regressor was trained using XgBoost regression algorithm on lexicon features. For news headlines track, an ensemble of regressors was used to predict sentiment score. One regressor was trained using TF-IDF features and another was trained using the n-gram features. The source code is available at Github.
HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods
Authors: Tobias Cabanski, Julia Romberg, Stefan Conrad
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: BIG-bench Machine Learning, Sentiment Analysis

In this Paper a system for solving SemEval-2017 Task 5 is presented. This task is divided into two tracks where the sentiment of microblog messages and news headlines has to be predicted. Since two submissions were allowed, two different machine learning methods were developed to solve this task, a support vector machine approach and a recurrent neural network approach. To feed in data for these approaches, different feature extraction methods are used, mainly word representations and lexica. The best submissions for both tracks are provided by the recurrent neural network which achieves a F1-score of 0.729 in track 1 and 0.702 in track 2.