Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets

TL;DR

This paper introduces the Multi-Layered Tweet Analyzer (MLTA), a novel graph-based method using multi-layered networks and graph neural networks to analyze and predict emotions in groups of tweets more accurately than traditional sentiment analysis.

Contribution

The paper presents a new algorithm that models social media texts with multi-layered networks, enabling better encoding of relationships and improved group-level emotion prediction.

Findings

MLTA predicts a wider range of emotions with higher accuracy.

Graph structures effectively capture complex relationships in social media data.

Group-level predictions outperform standard sentiment analysis methods.

Abstract

Anticipating audience reaction towards a certain piece of text is integral to several facets of society ranging from politics, research, and commercial industries. Sentiment analysis (SA) is a useful natural language processing (NLP) technique that utilizes both lexical/statistical and deep learning methods to determine whether different sized texts exhibit a positive, negative, or neutral emotion. However, there is currently a lack of tools that can be used to analyse groups of independent texts and extract the primary emotion from the whole set. Therefore, the current paper proposes a novel algorithm referred to as the Multi-Layered Tweet Analyzer (MLTA) that graphically models social media text using multi-layered networks (MLNs) in order to better encode relationships across independent sets of tweets. Graph structures are capable of capturing meaningful relationships in complex ecosystems compared to other representation methods. State of the art Graph Neural Networks (GNNs) are used to extract information from the Tweet-MLN and make predictions based on the extracted graph features. Results show that not only does the MLTA predict from a larger set of possible emotions, delivering a more accurate sentiment compared to the standard positive, negative or neutral, it also allows for accurate group-level predictions of Twitter data.