Sentiment Uncertainty and Spam in Twitter Streams and Its Implications for General Purpose Realtime Sentiment Analysis

TL;DR

This paper emphasizes the importance of explicitly modeling sentiment uncertainty in Twitter analysis, introduces a new dataset for evaluating such approaches, and discusses implications for real-time sentiment analysis.

Contribution

It introduces the concept of sentiment uncertainty in Twitter streams and provides a high-quality dataset for evaluating methods that address this challenge.

Findings

Over half of tweets exhibit sentiment ambiguity.

Explicit uncertainty modeling improves sentiment analysis accuracy.

A new dataset facilitates research on sentiment uncertainty.

Abstract

State of the art benchmarks for Twitter Sentiment Analysis do not consider the fact that for more than half of the tweets from the public stream a distinct sentiment cannot be chosen. This paper provides a new perspective on Twitter Sentiment Analysis by highlighting the necessity of explicitly incorporating uncertainty. Moreover, a dataset of high quality to evaluate solutions for this new problem is introduced and made publicly available.