Sentiment Analysis for Twitter : Going Beyond Tweet Text

TL;DR

This paper presents a sentiment analysis system for Twitter that improves accuracy by incorporating external URL information and social media features into tweet analysis.

Contribution

It introduces a novel approach that combines linguistic features with external URL data and social media features for enhanced sentiment detection in tweets.

Findings

Augmenting tweets with external URL information improves sentiment prediction.

Social media features significantly boost sentiment analysis accuracy.

The proposed method outperforms baseline models in sentiment detection.

Abstract

Analysing sentiment of tweets is important as it helps to determine the users' opinion. Knowing people's opinion is crucial for several purposes starting from gathering knowledge about customer base, e-governance, campaigning and many more. In this report, we aim to develop a system to detect the sentiment from tweets. We employ several linguistic features along with some other external sources of information to detect the sentiment of a tweet. We show that augmenting the 140 character-long tweet with information harvested from external urls shared in the tweet as well as Social Media features enhances the sentiment prediction accuracy significantly.