INF-UFRGS-OPINION-MINING at SemEval-2016 Task 6: Automatic Generation of a Training Corpus for Unsupervised Identification of Stance in Tweets

Dias, Marcelo Fernandes Pacheco; Becker, Karin

doi:10.18653/v1/s16-1061

Cited by 28 publications

(42 citation statements)

References 6 publications

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“…One way that was suggested to handle the unmentioned target entity in text is to analyze the opinion to the opponent of the entity or supporter of the entity. For example, [17] constructed a list of keywords that identifies Trump using a dataset labeled with stances toward Hillary. Using this list of keywords help in detecting the unexpressed stand towards Trump.…”

Section: Related Workmentioning

confidence: 99%

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

Aldayel

Magdy

2019

Proc. ACM Hum.-Comput. Interact.

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To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%.We further present an extensive analysis to show how these different set of features can reveal stance. Our findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic.

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Section: Related Workmentioning

confidence: 99%

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

Aldayel

Magdy

2019

Proc. ACM Hum.-Comput. Interact.

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“…By training conditional encoding models 3 Note that "|" indiates "or", ( ?) indicates optional space Dias and Becker (2016) on automatically labelled stance detection data we achieve state-of-the-art results. The best result (F1 of 0.5803) is achieved with the bi-directional conditional encoding model (BiCond).…”

Section: Resultsmentioning

confidence: 91%

“…The goal of experiments reported in this section is to compare against participants in the SemEval 2016 Stance Detection Task B. While we consider an unseen target setup, most submissions, including the three highest ranking ones for Task B, pkudblab (Wei et al, 2016), LitisMind (Zarrella andMarsh, 2016) and INF-UFRGS (Dias and Becker, 2016) considered a different experimental setup. They automatically annotated training data for the test target Donald Trump, thus converting the task into weakly supervised seen target stance detection.…”

Section: Weakly Supervised Stance Detectionmentioning

confidence: 99%

Stance Detection with Bidirectional Conditional Encoding

Augenstein¹,

Rocktäschel²,

Vlachos³

et al. 2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

292

214

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Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", "negative" or "neutral". Previous work has assumed that either the target is mentioned in the text or that training data for every target is given. This paper considers the more challenging version of this task, where targets are not always mentioned and no training data is available for the test targets. We experiment with conditional LSTM encoding, which builds a representation of the tweet that is dependent on the target, and demonstrate that it outperforms encoding the tweet and the target independently. Performance is improved further when the conditional model is augmented with bidirectional encoding. We evaluate our approach on the SemEval 2016 Task 6 Twitter Stance Detection corpus achieving performance second best only to a system trained on semi-automatically labelled tweets for the test target. When such weak supervision is added, our approach achieves state-of-the-art results.

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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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INF-UFRGS-OPINION-MINING at SemEval-2016 Task 6: Automatic Generation of a Training Corpus for Unsupervised Identification of Stance in Tweets

Cited by 28 publications

References 6 publications

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

Stance Detection with Bidirectional Conditional Encoding

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

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