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.

Saif M. Mohammad

Parinaz Sobhani

Svetlana Kiritchenko

ACM Transactions on Internet Technology

ACM TRANSACTIONS ON INTERNET TECHNOLOGY (TOIT) 汇集了许多计算学科，包括计算机软件工程、计算机编程语言、中间件、数据库管理、安全性、知识发现和数据挖掘、网络和分布式系统、通信、性能和可扩展性等。TOIT 将涵盖各个学科的结果和作用以及它们之间的关系。

ACM Transactions on Internet Technology (TOIT) brings together research from various computer science disciplines, including software engineering, programming languages, middleware, database management, security, knowledge discovery and data mining, networking and distributed systems, communication, performance, and scalability. TOIT focuses on the results and interrelations of these disciplines.

《ACM Transactions on Internet Technology (TOIT)》汇集了多个计算机学科的研究成果，包括计算机软件工程、编程语言、中间件、数据库管理、安全性、知识发现与数据挖掘、网络与分布式系统、通信、性能与可扩展性等。TOIT 关注这些学科的研究成果及其相互关系。

ACM Trans. Internet Technol.

Stance and Sentiment in Tweets

It is shown that although knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient and additional unlabeled data is used through distant supervision techniques and word embeddings to further improve stance classification.

Category	Quartile
计算机科学	3
计算机科学, 计算机信息系统	3
计算机科学, 计算机软件工程	3

Stance and Sentiment in Tweets

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Journal Info

Category	Quartile
COMPUTER SCIENCE, SOFTWARE ENGINEERING	1