Knowledge-Based Sentiment Analysis and Visualization on Social Networks

Vizcarra, Julio; Kozaki, Kouji; Torres-Ruiz, Miguel; Quintero, Rolando

doi:10.1007/s00354-020-00103-1

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…Correction of spelling mistakes and acronyms has been done. After that, the emotion symbols have been replaced by their describing words [ 49 ]. Tokenization is a fundamental step of NLP-related tasks in traditional count-vectorizer and advanced deep learning architectures.…”

Section: Proposed Methodologymentioning

confidence: 99%

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KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

Tiwari

Nagpal

2022

New Gener. Comput.

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Social media materialized as an influential platform that allows people to share their views on global and local issues. Sentiment analysis can handle these massive amounts of unstructured reviews and convert them into meaningful opinions. Undoubtedly, COVID-19 originated as the enormous challenge across the world that physically and financially bruted humankind. Meanwhile, farmers' protests shook up the world against three pieces of legislation passed by the Indian government. Hence, an artificial intelligence-based sentiment model is needed for suggesting the right direction toward outbreaks. Although Deep Neural Network (DNN) gained popularity in sentiment analysis applications, these still have a limitation of sequential training, high-dimension feature space, and equal feature importance distribution. In addition, inaccurate polarity scoring and utility-based topic modeling are other challenging aspects of sentiment analysis. It motivates us to propose a Knowledge-Enriched Attention-based Hybrid Transformer (KEAHT) model by enriching the explicit knowledge of Latent Dirichlet Allocation (LDA) topic modeling and lexicalized domain ontology. A pre-trained Bidirectional Encoder Representation from Transformer (BERT) is employed to train within a minimum training corpus. It provides the facility of attention mechanism and can solve complex text problems accurately. A comparative study with existing baselines and recent hybrid models affirms the credibility of the proposed KEAHT in the field of Natural Language Processing (NLP). This model emphasizes artificial intelligence's role in handling the situation of the global pandemic and democratic dispute in a country. Furthermore, two benchmark datasets, namely “COVID-19-Vaccine-Labelled-Tweets" and "Indian-Farmer-Protest-Labelled-Tweets”, are also constructed to accommodate future researchers for outlining the essential facts associated with the outbreaks.

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Section: Proposed Methodologymentioning

confidence: 99%

“…Correction of spelling mistakes and acronyms has been done. After that, the emotion symbols have been replaced by their describing words [ 49 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

Tiwari

Nagpal

2022

New Gener. Comput.

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“…For the cross-domain sentiment analysis prompted by the requirement to address the domain gap among different applications, Reference [8] took a novel perspective by introducing the external world knowledge to enhance the performance of sentiment analysis. Reference [37] proposed a knowledge-based methodology on social networks for sentiment analysis. This work focused on semantic processing considering the content by handling the opinions of public users as excerpts of knowledge.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-Guided Sentiment Analysis Via Learning From Natural Language Explanations

Sheng

et al. 2021

IEEE Access

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“…In [53], authors try to leverage a deep graph-based text representation of sentiment polarity by combining graphs and use them in a representation learning approach. The study presented in [54] proposes a knowledge-based methodology for Sentiment Analysis on social media, focusing primarily on semantics and on the content of words. The authors combine graph theory algorithms, similarity measures, knowledge graphs, and a disambiguation process to predict different range of feelings (joy, trust, sadness, and anger).…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification

2021

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The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative) in short documents, such as posts on Twitter. In this proposal, tweets are represented as graphs; then, graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and interpretability of the classification results, thanks to the integration of the Local Interpretable Model-agnostic Explanations (LIME) model at the end of the pipeline. LIME allows raising trust in predictive models, since the model is not a black box anymore. Uncovering the black box allows understanding and interpreting how the network could distinguish between sentiment polarities. Each phase of the proposed approach conformed by pre-processing, graph construction, dimensionality reduction, graph similarity, sentiment prediction, and interpretability steps is described. The proposal is compared with character n-gram embeddings-based Deep Learning models to perform Sentiment Analysis. Results show that the proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.

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Knowledge-Based Sentiment Analysis and Visualization on Social Networks

Cited by 13 publications

References 21 publications

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

Knowledge-Guided Sentiment Analysis Via Learning From Natural Language Explanations

Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification

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