Articles

This paper presents the approach of the GTI Research Group to SemEval-2016 task 4 on Sentiment Analysis in Twitter, or more specif- ically, subtasks A (Message Polarity Classifi- cation), B (Tweet classification according to a two-point scale) and D (Tweet quantification according to a two-point scale). We followed a supervised …...

We present an approach for tackling the tweet quantification problem in SemEval 2016. The approach is based on the creation of a co- occurrence graph per sentiment from the train- ing dataset and a graph per topic from the test dataset with the aim of comparing each topic graph against …...

The paper describes experiments on sentiment classification of microblog messages using an architecture allowing general machine learn- ing classifiers to be combined either sequen- tially to form a multi-step classifier, or in par- allel, creating an ensemble classifier. The sys- tem achieved very competitive results in the shared task on …...

In this paper, we propose a deep learning sys- tem for classification of tweets on a two-point scale. Our architecture consists of a multilay- ered recurrent neural network having gated re- current units. The network is pre-trained with a weakly labeled dataset of tweets to learn the sentiment specific embeddings. …...

This paper describes the system with which we participated in SemEval-2016 Task 4 (Sen- timent Analysis in Twitter) and specifically the Message Polarity Classification subtask. Our system is a weighted ensemble of two sys- tems. The first one is based on a previous sentiment analysis system and uses manually crafted …...

This paper presents our approach for SemEval 2016 task 4: Sentiment Analysis in Twitter. We participated in Subtask A: Message Polar- ity Classification. The aim is to classify Twit- ter messages into positive, neutral, and nega- tive polarity. We used a lexical resource for pre-processing of social media data and …...

This paper details on the participation of ISTI- CNR to task 4 of Semeval 2016. Among the five subtasks, special attention has been paid to the five-point scale quantification sub- task. The quantification method we pro- pose is based on the observation that a standard document-by-document regression method usually has …...

This paper describes the participation of the team “TwiSE” in the SemEval 2016 challenge. Specifically, we participated in Task 4, namely “Sentiment Analysis in Twitter” for which we implemented sentiment classification systems for subtasks A, B, C and D. Our approach consists of two steps. In the first step, we …...

In this paper we describe our deep learning ap- proach for solving both two-, three- and five- class tweet polarity classification, and two- and five-class quantification. We first trained a convolutional neural network using pretrained Twitter word embeddings, so that we could extract the hidden activation values from the hidden …...

In this work, we apply classifier fusion to tweet polarity identification problem. The task is to predict whether the emotion hidden in a tweet is positive, neutral, or negative. An asymmetric SIMPLS (ASIMPLS) based clas- sifier, which was proved to be able to identify the minority class well in imbalanced …...

This paper describes our approach to the Se- mEval 2016 task 4, “Sentiment Analysis in Twitter”, where we participated in subtask A. Our system relies on AlchemyAPI and Senti- WordNet to create 43 features based on which we select a feature subset as final represen- tation. Active Learning then filters …...

We describe the systems we have used for participating in Subtasks D (binary quan- tification) and E (ordinal quantification) of SemEval-2016 Task 4 “Sentiment Analysis in Twitter”. The binary quantification sys- tem uses a “Probabilistic Classify and Count” (PCC) approach that leverages the calibrated probabilities obtained from the output of …...

In this paper we describe a deep learn- ing system that has been built for SemEval 2016 Task4 (Subtask A and B). In this work we trained a Gated Recurrent Unit (GRU) neural network model on top of two sets of word embeddings: (a) general word embed- dings generated from …...

Here for the first time we present a shared task on detecting stance from tweets: given a tweet and a target entity (person, organiza- tion, etc.), automatic natural language systems must determine whether the tweeter is in favor of the given target, against the given target, or whether neither inference …...

This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. …...

This paper discusses the fourth year of the ”Sentiment Analysis in Twitter Task”. SemEval-2016 Task 4 comprises five sub- tasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards …...

We present a shared task on automatically de- termining sentiment intensity of a word or a phrase. The words and phrases are taken from three domains: general English, English Twit- ter, and Arabic Twitter. The phrases include those composed of negators, modals, and de- gree adverbs as well as phrases …...

We describe a question answering model that applies to both images and structured knowl- edge bases. The model uses natural lan- guage strings to automatically assemble neu- ral networks from a collection of composable modules. Parameters for these modules are learned jointly with network-assembly param- eters via reinforcement learning, with …...

We show how eye-tracking corpora can be used to improve sentence compression mod- els, presenting a novel multi-task learning al- gorithm based on multi-layer LSTMs. We ob- tain performance competitive with or better than state-of-the-art approaches. 1...

We introduce a novel, simple convolution neu- ral network (CNN) architecture – multi-group norm constraint CNN (MGNC-CNN) – that capitalizes on multiple sets of word embed- dings for sentence classification. MGNC- CNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network …...