Articles

We present the INESC-ID system for the 2016 edition of SemEval Twitter Sentiment Analy- sis shared task (subtask 4-A). The system was based on the Non-Linear Sub-space Embed- ding (NLSE) model developed for last year’s competition. This model trains a projection of pre-trained embeddings into a small sub- space using …...

This paper presents an alternative approach to polarity and intensity classification of senti- ments in microblogs. In contrast to previous works, which either relied on carefully de- signed hand-crafted feature sets or automat- ically derived neural embeddings for words, our method harnesses character embeddings as its main input units. We …...

This paper describes the submission of team IIP in SemEval-2016 Task 4 Subtask A. The presented system is a novel weighted sum ensemble approach for sentiment analysis of short informal texts. The ensemble com- bines member classifiers that output classifi- cation confidence metrics. For the ensemble classification decision the members …...

The paper describes our submission to the task on Sentiment Analysis on Twitter at SemEval 2016. The approach is based on a Deep Learning architecture using convolu- tional neural networks. The approach used on- ly word embeddings as features. The submis- sion used embeddings created from a corpus of news …...

This paper reports our submissions to Task 4, i.e., Sentiment Analysis in Twitter (SAT), in SemEval 2016, which consists of five subtasks grouped into two levels: (1) sentence level, i.e., message polarity classification (subtask A), and (2) topic level, i.e., tweet classification and quantification according to two-point s- cale (subtask …...

The aim of this paper is to produce a method- ology for analyzing sentiments of selected Twitter messages, better known as Tweets. This project elaborates on two experiments carried out to analyze the sentiment of Tweets from SemEval-2016 Task 4 Subtask A and Subtask B. Our method is built from …...

We present our supervised sentiment classifi- cation system which competed in SemEval- 2016 Task 4: Sentiment Analysis in Twitter. Our system employs a Support Vector Ma- chine (SVM) classifier trained using a num- ber of features including n-grams, synset ex- pansions, various sentiment scores, word clus- ters, and term centroids. …...

This paper describes the system developed at LIF for the SemEval-2016 evaluation cam- paign. The goal of Task 4.A was to iden- tify sentiment polarity in tweets. The system extends the Convolutional Neural Networks (CNN) state of the art approach. We ini- tialize the input representations with embed- dings trained …...

This paper contains the description of our par- ticipation at task 4 (sub-task A, Message Po- larity Classification) of SemEval-2016. Our proposed system consists mainly of three steps. Firstly, the preprocessing step includes the tokenization and identification of special elements including URLs, hashtags, user men- tions and emoticons. The second …...

This paper describes our sentiment analysis system which has been built for Sentiment Analysis in Twitter Task of SemEval-2016. We have used a Logistic Regression classifier with different groups of features. This sys- tem is an improvement to our previous sys- tem Lsislif in Semeval-2015 after removing some features and …...

This paper describes our sentiment clas- sification system for microblog-sized doc- uments, and documents where a topic is present. The system consists of a soft- voting ensemble of a word2vec language model adapted to classification, a convolu- tional neural network (CNN), and a long- short term memory network (LSTM). Our …...

This paper describes our deep learning-based approach to sentiment analysis in Twitter as part of SemEval-2016 Task 4. We use a convolutional neural network to determine sentiment and participate in all subtasks, i.e. two-point, three-point, and five-point scale sentiment classification and two-point and five-point scale sentiment quantification. We achieve competitive …...

In this paper, we present a transfer learning sys- tem for twitter sentiment classification and compare its performance using different fea- ture sets that include different word represen- tation vectors. We utilized data from a different source domain to increase the performance of our system in the target domain. Our …...

In this paper, we present a novel deep learning architecture for sentiment analysis in Twitter messages. Our system finki, employs both convolutional and gated recurrent neural net- works to obtain a more diverse tweet repre- sentation. The network is trained on top of GloVe word embeddings pre-trained on the Common …...

We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the sub- task B. Our system comprises of multiple in- dependent models such as neural networks, semantic-affective models and topic modeling that are combined in a probabilistic way. The novelty of the …...

This paper describes our system submitted for the Sentiment Analysis in Twitter task of SemEval-2016, and specifically for the Mes- sage Polarity Classification subtask. We used a system that combines Convolutional Neural Networks and Logistic Regression for senti- ment prediction, where the former makes use of embedding features while the …...

This work presents our team solution for task 4a (Message Polarity Classification) at the Se- mEval 2016 challenge. Our experiments have been carried out over the Twitter dataset pro- vided by the challenge. We follow a super- vised approach, exploiting a SVM polyno- mial kernel classifier trained with the chal- …...

This paper describes a classification system that participated in SemEval-2016 Task 4: Sentiment Analysis in Twitter. The proposed approach competed in subtasks A, B, and C, which involved tweet polarity classification, tweet classification according to a two-point scale, and tweet classification according to a five-point scale. Our system is based …...

In many areas, such as social science, politics or market research, people need to track sentiment and their changes over time. For sentiment analysis in this field it is more important to correctly esti- mate proportions of each sentiment expressed in the set of documents (quantification task) than to accurately …...

This paper introduces aicyber’s systems for SemEval 2016 , Task 4A. The first system is build on vector space model (VSM), the second system is build on a new framework to estimate sentence vector, it is inspired by the i-vector in speaker verification domain. Both systems are evaluated on SemEval …...