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

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. Knowled...

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Autores principales:	Fernando Andres Lovera, Yudith Coromoto Cardinale, Masun Nabhan Homsi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sentiment analysis knowledge graph Long-Short Term Memory (LSTM) model interpretability Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/cf6e45e70a73437ab9b786e614197a1a
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id	oai:doaj.org-article:cf6e45e70a73437ab9b786e614197a1a
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spelling	oai:doaj.org-article:cf6e45e70a73437ab9b786e614197a1a2021-11-25T17:24:08ZSentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification10.3390/electronics102227392079-9292https://doaj.org/article/cf6e45e70a73437ab9b786e614197a1a2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2739https://doaj.org/toc/2079-9292The 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%.Fernando Andres LoveraYudith Coromoto CardinaleMasun Nabhan HomsiMDPI AGarticlesentiment analysisknowledge graphLong-Short Term Memory (LSTM)model interpretabilityElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2739, p 2739 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sentiment analysis knowledge graph Long-Short Term Memory (LSTM) model interpretability Electronics TK7800-8360
spellingShingle	sentiment analysis knowledge graph Long-Short Term Memory (LSTM) model interpretability Electronics TK7800-8360 Fernando Andres Lovera Yudith Coromoto Cardinale Masun Nabhan Homsi Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification
description	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%.
format	article
author	Fernando Andres Lovera Yudith Coromoto Cardinale Masun Nabhan Homsi
author_facet	Fernando Andres Lovera Yudith Coromoto Cardinale Masun Nabhan Homsi
author_sort	Fernando Andres Lovera
title	Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification
title_short	Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification
title_full	Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification
title_fullStr	Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification
title_full_unstemmed	Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification
title_sort	sentiment analysis in twitter based on knowledge graph and deep learning classification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cf6e45e70a73437ab9b786e614197a1a
work_keys_str_mv	AT fernandoandreslovera sentimentanalysisintwitterbasedonknowledgegraphanddeeplearningclassification AT yudithcoromotocardinale sentimentanalysisintwitterbasedonknowledgegraphanddeeplearningclassification AT masunnabhanhomsi sentimentanalysisintwitterbasedonknowledgegraphanddeeplearningclassification
_version_	1718412438436577280

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

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