Stance and Sentiment in Tweets

By Saif M. Mohammad, Parinaz Sobhani, Svetlana Kiritchenko. ACM Trans. Internet Techn., 2016

489 citations recorded by the source corpus.

We can often detect from a person’s utterances whether he or she is in favor of or against a given target entity—one’s stance toward the target. However, a person may express the same stance toward a target by using negative or positive language. Here for the first time we present a dataset of tweet–target pairs annotated for both stance and sentiment. The targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. Partitions of this dataset were used as training and test sets in a SemEval-2016 shared task competition. We propose a simple stance detection system that outperforms submissions from all 19 teams that participated in the shared task. Additionally, access to both stance and sentiment annotations allows us to explore several research questions. We show that although knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient. Finally, we use additional unlabeled data through distant supervision techniques and word embeddings to further improve stance classification.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/b3b79831a3bdbbc50989d1d82737d44694cd67c8

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Citations in the corpus

SemEval-2018 Task 1: Affect in Tweets (2018, background): “There is also growing work on related tasks such as stance detection (Mohammad et al., 2017) and argumentation mining (Wojatzki et al., 2018; Palau and Moens, 2009).”
Zero-Shot Stance Detection: A Dataset and Model Using Generalized Topic Representations (2020, background): “Stance detection, automatically identifying positions on a speciﬁc topic in text (Mohammad et al., 2017), is crucial for understanding how information is presented in everyday life.”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “The first shared task on detecting stance in tweets was organized in 2016 Mohammad, Kiritchenko, Sobhani, Zhu, and Cherry (2016), Mohammad, Sobhani, and Kiritchenko (2017) .”
ConStance: Modeling Annotation Contexts to Improve Stance Classification (2017, methodology): “Classiﬁer performance on the GS is measured, following prior work (Mohammad et al., 2016a; Ebrahimi et al., 2016), on the average of the F1 scores on the two classes of interest (“Clinton” and “Trump”).”
Stance Detection on Social Media: State of the Art and Trends (2020, methodology): “It has been shown that stance could be inferred independently from author’s emotion state [Li and Caragea 2019a; Sobhani et al. 2016; Mohammad et al. 2017; Mohammad 2017].”
How would Stance Detection Techniques Evolve after the Launch of ChatGPT? (2022, methodology): “The effective algorithms for the classiﬁers are support vector machine (SVM) (Addawood et al., 2017; Mohammad et al., 2017), logistic regression (Fer-reira and Vlachos, 2016; Tsakalidis et al., 2018; Skeppstedt et al., 2017), naive bayes (HaCohen-Kerner et al., 2017; Simaki et al., 2017), decision…”
Fake News Detection on Social Media: A Data Mining Perspective (2017, methodology): “Stance detection is the task of automatically determining from a post whether the user is in favor of, neutral toward, or against some target entity, event, or idea [53].”
Challenges in Sentiment Analysis (2017, background): “Surveys by Pang and Lee (2008), Liu and Zhang (2012), and Mohammad (2016b) give summaries of the many automatic classiﬁers, features, and datasets used to detect sentiment.”