Stance and Sentiment in Tweets (1605.01655v1)

Abstract: We can often detect from a person's utterances whether he/she is in favor of or against a given target entity -- their stance towards the target. However, a person may express the same stance towards 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 while 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.

Analysis of "Stance and Sentiment in Tweets"

The paper "Stance and Sentiment in Tweets" by Saif M. Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko presents a comprehensive exploration of stance detection in the domain of Twitter, providing a novel dataset that diverges from traditional sentiment analysis. Unlike sentiment, which is inherently target-agnostic, stance detection focuses on determining an author's attitude—favor, against, or neither—towards a specified target that may not be explicitly mentioned in the text. This distinction introduces a layer of complexity significant for applications in information retrieval, opinion mining, and social media analysis.

Core Contributions

1. Stance Dataset Development: The authors introduce a pioneering dataset consisting of tweet-target pairs annotated for stance as well as sentiment. This dataset was utilized in the SemEval-2016 Task 6 shared task competition, highlighting its relevance and applicability. The dataset spans multiple categorical targets, such as 'Hillary Clinton' and 'Climate Change is a Real Concern', with each tweet annotated not only for stance but also for sentiment and whether the target of opinion aligns with the target of interest.
2. Stance Detection System: The paper proposes a stance detection system that surpasses the performance of more complex models, such as those incorporating recurrent neural networks, achieving an impressive F-score of 70.3. The model is built on a linear-kernel SVM classifier and utilizes features like word and character $n$-grams, enhancing it further with sentiment-derived features from specialized lexicons and word embeddings crafted through distant supervision.
3. Theoretical Insights into Stance and Sentiment: A significant insight from their findings is that, although sentiment features are vital in sentiment analysis, they fall short in adequately predicting stance. A detailed examination reveals that while positive and negative sentiment features aid sentiment classification, they offer minimal improvement for stance detection—underscoring that sentiment and stance are distinct constructs. The paper further demonstrates the challenge of stance detection when expressed opinions are aimed at entities other than the main target.

Methodology

The authors approached the research by first gathering and annotating a novel dataset that includes multivalent annotations for both stance and sentiment. They meticulously curated this data from Twitter by leveraging stance-indicative hashtags that aid in automatic label extraction, defining a new field for distant supervision methodologies in stance attribution. They explore two major lines of feature engineering: (1) use of manually and automatically identified hashtags to create pseudo-labeled corpora and (2) extraction of word-stance and word-target associations as novel features.

The robust experimental setup employed for stance determination includes leveraging these pseudo-labeled corpora and extends to adopting word embeddings, extracted from a substantial corpus of tweets. The insightful comparative evaluation of these innovative approaches against baselines, such as Oracle systems relying solely on sentiment and opinion target knowledge, illustrates their contribution to modeling stance effectively.

Implications and Future Directions

The development of the stance dataset and the subsequent empirical findings have broad implications. Practically, stance detection systems can enhance social media monitoring tools by accurately gauging public opinion and aligning strategic communications accordingly. Theoretically, the work opens avenues for exploring the complex interplay between target-specific opinions and general sentiment expressions, a topic ripe for future exploration.

The paper hints at possible directions for future research, such as using more advanced classifiers like those integrating deep learning to model stance and sentiment jointly. Moreover, the prospect of building stance detection models that function well across domains and without direct target-specific training data suggests potential advancements in transfer learning applications.

By aligning stance detection methodologies with robust sentiment frameworks, the paper provides valuable insights for researchers aiming to develop systems that can more effectively interpret the nuance and context that social media language embodies. The endeavors demonstrated in this work set a benchmark and propose a scalable and insightful path forward for stance detection research in natural language processing.