# Assessing Sentiment of the Expressed Stance on Social Media

## TL;DR

This paper investigates the relationship between sentiment polarity and expressed stance on social media, revealing that sentiment alone is insufficient to accurately determine viewpoint, through new dataset analysis and validation on existing benchmarks.

## Contribution

It introduces a new dataset for stance and sentiment analysis and demonstrates that sentiment polarity does not reliably indicate stance, challenging common assumptions.

## Key findings

- Sentiment and stance are not highly correlated.
- Simple sentiment polarity cannot reliably determine stance.
- Analysis validated on SemEval stance dataset.

## Abstract

Stance detection is the task of inferring viewpoint towards a given topic or entity either being supportive or opposing. One may express a viewpoint towards a topic by using positive or negative language. This paper examines how the stance is being expressed in social media according to the sentiment polarity. There has been a noticeable misconception of the similarity between the stance and sentiment when it comes to viewpoint discovery, where negative sentiment is assumed to mean against stance, and positive sentiment means in-favour stance. To analyze the relation between stance and sentiment, we construct a new dataset with four topics and examine how people express their viewpoint with regards these topics. We validate our results by carrying a further analysis of the popular stance benchmark SemEval stance dataset. Our analyses reveal that sentiment and stance are not highly aligned, and hence the simple sentiment polarity cannot be used solely to denote a stance toward a given topic.