UWB at SemEval-2016 Task 6: Stance Detection

Krejzl, Peter; Shinar, J.

doi:10.18653/v1/s16-1066

Cited by 22 publications

(15 citation statements)

References 10 publications

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“…At the feature extraction phase, the following feature types were generated [25], [30]: word and character n-grams, dependency features, target features, stanceindicative features, linguistic features, stylistic features, sentiment features and word embeddings (Table I). These feature types are described in detail below.…”

Section: B Feature Extractionmentioning

confidence: 99%

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A New Method for Stance Detection Based on Feature Selection Techniques and Ensembles of Classifiers

Vychegzhanin

Kotelnikov²

2021

IEEE Access

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Stance detection is one of the promising areas of computational linguistics, the task of which is to automatically recognize the author's viewpoint on the target object. In our study, to detect the stance, we propose the Ensemble-based Stance Detection method (ESD). First, we calculate the optimal number of features that are most relevant to the given domain based on the function approximating the dependence of F1-score on the number of features. Then we form a relevant feature set using the homogeneous ensemble of feature selection methods. At last, we build the optimal composition of classifiers using the crossvalidation procedure. Furthermore, we study the impact of various feature types on the performance in the stance detection task. The proposed ESD method is evaluated on the SemEval-2016 text corpus of tweets and the UKP Sentential Argument Mining corpus, and it outperforms the state-of-the-art systems.

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Section: B Feature Extractionmentioning

confidence: 99%

“…Stance-indicative features are formed on the basis of Favor and Against lists generated as proposed in [30]. For each word the ratio of its frequency in the texts of the favor class to its frequency in the whole training corpus is calculated.…”

Section: Stance-indicative Featuresmentioning

confidence: 99%

A New Method for Stance Detection Based on Feature Selection Techniques and Ensembles of Classifiers

Vychegzhanin

Kotelnikov²

2021

IEEE Access

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“…Table III shows the comparison of our proposed system with the existing state-of-the-art system of SemEval 2016 Task 6 for the sentiment dataset. [8] used feature-based SVM, [40] used keyword rules, LitisMind relied on hashtag rules on external data, [39] utilized a combination of sentiment classifiers and rules, whereas [38] used a maximum entropy classifier with domain-specific features. Our system comfortably surpasses the existing best system at SemEval.…”

Section: Modelsmentioning

confidence: 99%

Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis

Abhishek

Ekbal

Kawahra

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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In this paper, we propose a two-layered multi-task attention based neural network that performs sentiment analysis through emotion analysis. The proposed approach is based on Bidirectional Long Short-Term Memory and uses Distributional Thesaurus as a source of external knowledge to improve the sentiment and emotion prediction. The proposed system has two levels of attention to hierarchically build a meaningful representation. We evaluate our system on the benchmark dataset of SemEval 2016 Task 6 and also compare it with the state-of-theart systems on Stance Sentiment Emotion Corpus. Experimental results show that the proposed system improves the performance of sentiment analysis by 3.2 F-score points on SemEval 2016 Task 6 dataset. Our network also boosts the performance of emotion analysis by 5 F-score points on Stance Sentiment Emotion Corpus.

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“…Task A External resources: More than 1.5 million of tweets were added by using representative hashtag for target-stance pairs. UWB [20] Overall approach: Maximum entropy classifier. Tasks A and B External resources: N-grams, PoS labels, General Inquirer.…”

Section: Our Approachmentioning

confidence: 99%

Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets

Lai

Farías

Patti

et al. 2017

Advances in Computational Intelligence

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Abstract. Stance detection, the task of identifying the speaker's opinion towards a particular target, has attracted the attention of researchers. This paper describes a novel approach for detecting stance in Twitter. We define a set of features in order to consider the context surrounding a target of interest with the final aim of training a model for predicting the stance towards the mentioned targets. In particular, we are interested in investigating political debates in social media. For this reason we evaluated our approach focusing on two targets of the SemEval-2016 Task 6 on Detecting stance in tweets, which are related to the political campaign for the 2016 U.S. presidential elections: Hillary Clinton vs. Donald Trump. For the sake of comparison with the state of the art, we evaluated our model against the dataset released in the SemEval-2016 Task 6 shared task competition. Our results outperform the best ones obtained by participating teams, and show that information about enemies and friends of politicians help in detecting stance towards them.

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UWB at SemEval-2016 Task 6: Stance Detection

Cited by 22 publications

References 10 publications

A New Method for Stance Detection Based on Feature Selection Techniques and Ensembles of Classifiers

A New Method for Stance Detection Based on Feature Selection Techniques and Ensembles of Classifiers

Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis

Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets

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