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

• URL: http://arxiv.org/abs/2207.00907v1
• Date: Sat, 2 Jul 2022 20:26:55 GMT
• Title: Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets
• Authors: Anna Nguyen, Antonio Longa, Massimiliano Luca, Joe Kaul, Gabriel Lopez
• Abstract summary: The paper proposes a novel algorithm that graphically models social media text using multi-layered networks (MLNs) in order to better encode relationships across independent sets of tweets. 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.
• Score: 0.10499611180329801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models [1.342834401139078]
  This work uses a lexicon-based method to perform sentiment analysis and shows an evaluation of classification models trained over textual data. The lexicon-based methods identify the intensity of emotion and subjectivity at word levels. This work is based on a multi-class problem of text being labeled as positive, negative, or neutral.
  arXiv  Detail & Related papers  (2024-09-19T15:31:12Z)
• Learning Multiplex Representations on Text-Attributed Graphs with One Language Model Encoder [55.24276913049635]
  We propose METAG, a new framework for learning Multiplex rEpresentations on Text-Attributed Graphs. In contrast to existing methods, METAG uses one text encoder to model the shared knowledge across relations. We conduct experiments on nine downstream tasks in five graphs from both academic and e-commerce domains.
  arXiv  Detail & Related papers  (2023-10-10T14:59:22Z)
• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings [20.25180279903009]
  We propose Contrastive Graph-Text pretraining (ConGraT) for jointly learning separate representations of texts and nodes in a text-attributed graph (TAG) Our method trains a language model (LM) and a graph neural network (GNN) to align their representations in a common latent space using a batch-wise contrastive learning objective inspired by CLIP. Experiments demonstrate that ConGraT outperforms baselines on various downstream tasks, including node and text category classification, link prediction, and language modeling.
  arXiv  Detail & Related papers  (2023-05-23T17:53:30Z)
• Universal Multimodal Representation for Language Understanding [110.98786673598015]
  This work presents new methods to employ visual information as assistant signals to general NLP tasks. For each sentence, we first retrieve a flexible number of images either from a light topic-image lookup table extracted over the existing sentence-image pairs. Then, the text and images are encoded by a Transformer encoder and convolutional neural network, respectively.
  arXiv  Detail & Related papers  (2023-01-09T13:54:11Z)
• Multiplex Graph Neural Network for Extractive Text Summarization [34.185093491514394]
  Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. We propose a novel Multiplex Graph Convolutional Network (Multi-GCN) to jointly model different types of relationships among sentences and words. Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extractive text summarization.
  arXiv  Detail & Related papers  (2021-08-29T16:11:01Z)
• FiLMing Multimodal Sarcasm Detection with Attention [0.7340017786387767]
  Sarcasm detection identifies natural language expressions whose intended meaning is different from what is implied by its surface meaning. We propose a novel architecture that uses the RoBERTa model with a co-attention layer on top to incorporate context incongruity between input text and image attributes. Our results demonstrate that our proposed model outperforms the existing state-of-the-art method by 6.14% F1 score on the public Twitter multimodal detection dataset.
  arXiv  Detail & Related papers  (2021-08-09T06:33:29Z)
• Exploiting BERT For Multimodal Target SentimentClassification Through Input Space Translation [75.82110684355979]
  We introduce a two-stream model that translates images in input space using an object-aware transformer. We then leverage the translation to construct an auxiliary sentence that provides multimodal information to a language model. We achieve state-of-the-art performance on two multimodal Twitter datasets.
  arXiv  Detail & Related papers  (2021-08-03T18:02:38Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• Be More with Less: Hypergraph Attention Networks for Inductive Text Classification [56.98218530073927]
  Graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are unable to capture high-order interaction between words. We propose a principled model -- hypergraph attention networks (HyperGAT) which can obtain more expressive power with less computational consumption for text representation learning.
  arXiv  Detail & Related papers  (2020-11-01T00:21:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.