Articles

This paper describes the iLab-Edinburgh Sen- timent Analysis system, winner of the Ara- bic Twitter Task 7 in SemEval-2016. The system employs a hybrid approach of super- vised learning and rule-based methods to pre- dict a sentiment intensity (SI) score for a given Arabic Twitter phrase. First, the supervised method …...

In this paper, we present our contribution in SemEval2016 task71: Determining Senti- ment Intensity of English and Arabic Phrases, where we use web search engines for English and Arabic unsupervised sentiment intensity prediction. Our work is based, first, on a group of classic sentiment lexicons (e.g. Sen- timent140 Lexicon, SentiWordNet). …...

This paper describes our system for the detec- tion of stances in tweets submitted to SemEval 2016 Task 6A. The system uses an ensemble of learning algorithms, fine-tuned using a genetic algorithm. We experiment with various off- the-shelf classifiers and build our model using standard lexical and a number of …...

We describe MITRE’s submission to the SemEval-2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, pro- ducing an average F1 score of 67.8 when as- sessing whether a tweet author was in favor or against a topic. We employed …...

This paper describes our submissions to Task 6, i.e., Detecting Stance in Tweets, in SemEval 2016, which aims at detecting the stance of tweets towards given target. There are three stance labels: Favor (directly or indirectly by supporting given target), Against (directly or indirectly by opposing or criticizing given tar- …...

This paper describes an approach to auto- amatically detect stance in tweets by building supervised system combining shallow features and pre-trained word vectors as word representation. The word vectors were obtained from several collections of large corpora using GloVe, an unsupervised learn- ing algorithm. We created feature vectors by selecting …...

We describe the system submitted to the SemEval-2016 for detecting stance in tweets (Task 6, Subtask A). One of the main goals of stance detection is to automatically determine the stance of a tweet towards a specific tar- get as ‘FAVOR’, ‘AGAINST’, or ‘NONE’. We developed a supervised system using …...

We present a supervised system that uses lex- ical, sentiment, semantic dictionaries and la- tent and frame semantic features to identify the stance of a tweeter towards an ideologi- cal target. We evaluate the performance of the proposed system on subtask A in SemEval- 2016 Task 6: “Detecting Stance in …...

In this paper, we describe our participation in the first shared task on automated stance de- tection (SemEval 2016 Task 6). We consider the task as a multidimensional classification problem and thus use a sequence of stacked classifiers. For subtask A, we utilize a rich feature set that does not …...

Stance classification aims to identify, for a particular issue under discussion, whether the speaker or author of a conversational turn has Pro (Favor) or Con (Against) stance on the is- sue. Detecting stance in tweets is a new task proposed for SemEval-2016 Task6, involv- ing predicting stance for a dataset …...

This paper describes our approach for the De- tecting Stance in Tweets task (SemEval-2016 Task 6). We utilized recent advances in short text categorization using deep learning to cre- ate word-level and character-level models. The choice between word-level and character- level models in each particular case was in- formed through …...

In this paper, we compare feature-based and Neural Network-based approaches on the su- pervised stance classification task for tweets in SemEval-2016 Task 6 Subtask A (Moham- mad et al., 2016). In the feature-based ap- proach, we use external resources such as lex- icons and crawled texts. The Neural Network based …...

This paper describes our system participating in the SemEval 2016 task: Detecting stance in Tweets. The goal was to identify whether the author of a tweet is in favor of the given target or against. Our approach is based on a maximum entropy classifier, which uses surface-level, sentiment and domain-specific …...

We present the IUCL system, based on super- vised learning, for the shared task on stance detection. Our official submission, the ran- dom forest model, reaches a score of 63.60, and is ranked 6th out of 19 teams. We also use gradient boosting decision trees and SVM and merge all …...

This paper describes the University of Sheffield’s submission to the SemEval 2016 Twitter Stance Detection weakly supervised task (SemEval 2016 Task 6, Subtask B). In stance detection, the goal is to classify the stance of a tweet towards a target as “favor”, “against”, or “none”. In Subtask B, the targets …...

In this paper, we develop a convolutional neu- ral network for stance detection in tweets. Ac- cording to the official results, our system ranks 1st on subtask B (among 9 teams) and ranks 2nd on subtask A (among 19 teams) on the twitter test set of SemEval2016 Task 6. The …...

This paper describe a weakly supervised so- lution for detecting stance in tweets, submit- ted to the SemEval 2016 Stance Task. Our approach is based on the premise that stance can be exposed as positive or negative opin- ions, although not necessarily about the stance target itself. Our system receives …...

This paper describes our system for aspect- based sentiment analysis (ABSA). We partic- ipate in Subtask 1 (sentence-level ABSA), fo- cusing specifically on aspect category detec- tion. We train a binary classifier for each category. This year’s addition of multiple languages makes language-independent ap- proaches attractive. We propose to utilize …...

This paper describes our system for Aspect- Based Sentiment Analysis (ABSA), task 5 of SemEval 2016. To conduct sentence level ABSA, we employed minimally supervised approaches for each type of extracted infor- mation. The system uses Word2Vec to derive word semantic similarities, and relies on ex- ternal review corpora as …...

This paper describes our systems submitted to the Sentence-level and Text-level Aspect- Based Sentiment Analysis (ABSA) task (i.e., Task 5) in SemEval-2016. The task involves two phases, namely, Aspect Detection phase and Sentiment Polarity Classification phase. We participated in the second phase of both subtasks in laptop and restaurant domains, …...