mlsanityData to 7 Oct 2026

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

arXiv 2207.00907 ↗First posted 2 Jul 2022cs.AI cs.SIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 64 references that are in the map. First posted on arXiv 2 Jul 2022.

Builds on

23 of 64 references.

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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Predictioncited by 4
  2. 2023Reducing Computational Costs in Sentiment Analysis: Tensorized Recurrent Networks vs. Recurrent Networkscited by 0
  3. 2024Analysing the Behaviour of Tree-Based Neural Networks in Regression Taskscited by 0