PANAS-t: A Pychometric Scale for Measuring Sentiments on Twitter

TL;DR

This paper introduces PANAS-t, a psychometric scale-based method for accurately measuring public sentiments from Twitter data, validated across diverse real-world events and large-scale tweet datasets.

Contribution

It develops and tests a novel sentiment measurement tool based on PANAS, tailored for short Twitter texts, demonstrating its effectiveness across multiple event types.

Findings

PANAS-t accurately captures sentiments during major events

It works effectively on large-scale Twitter datasets

Sentiment trends align with expected public reactions

Abstract

Online social networks have become a major communication platform, where people share their thoughts and opinions about any topic real-time. The short text updates people post in these network contain emotions and moods, which when measured collectively can unveil the public mood at population level and have exciting implications for businesses, governments, and societies. Therefore, there is an urgent need for developing solid methods for accurately measuring moods from large-scale social media data. In this paper, we propose PANAS-t, which measures sentiments from short text updates in Twitter based on a well-established psychometric scale, PANAS (Positive and Negative Affect Schedule). We test the efficacy of PANAS-t over 10 real notable events drawn from 1.8 billion tweets and demonstrate that it can efficiently capture the expected sentiments of a wide variety of issues spanning tragedies, technology releases, political debates, and healthcare.