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Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

José Antonio García-Díaz, Mar Cánovas-García, Rafael Valencia-García
2020 Future generations computer systems  
We intend to fill these gaps proposing an ontology-driven aspect-based sentiment analysis with which to measure the general public's opinions as regards infectious diseases when expressed in Spanish by  ...  employing a case study of tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America.  ...  Acknowledgements This work has been supported by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER/ERDF) through projects KBS4FIA (TIN2016-76323-R) and LaTe4PSP  ... 
doi:10.1016/j.future.2020.06.019 pmid:32572291 pmcid:PMC7301140 fatcat:xxt6mfojevf3zhpu4fchr3lzuq

Novel Semantics-based Distributed Representations for Message Polarity Classification using Deep Convolutional Neural Networks

Abhinay Pandya, Mourad Oussalah
2017 Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management  
We examine the effects of these features incorporated in a convolutional neural network(CNN) model for evaluation on the SemEval benchmarked dataset.  ...  In this paper, we propose three semantics-based distributed representations of words and phrases as features for message polarity classification: Sentiment-Specific Multi-Word Expressions Embeddings(SSMWE  ...  ACKNOWLEDGEMENTS We would like to thank the anonymous reviewers for their valuable suggestions because of which the technical quality of the work presented in this paper has improved.  ... 
doi:10.5220/0006500800710082 dblp:conf/ic3k/PandyaO17 fatcat:kx3t2cb32rg5tite6joaziknbm

Deep Learning for Sentiment Analysis : A Survey [article]

Lei Zhang, Shuai Wang, Bing Liu
2018 arXiv   pre-print
This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.  ...  Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years.  ...  Acknowledgments Bing Liu and Shuai Wang's work was supported in part by National Science Foundation (NSF) under grant no. IIS1407927 and IIS-1650900, and by Huawei Technologies Co.  ... 
arXiv:1801.07883v2 fatcat:nplicfgaozb6fbfx4eyts4zt7e

AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

Niloufar Shoeibi, Nastaran Shoeibi, Guillermo Hernández, Pablo Chamoso, Juan M. Corchado
2021 Electronics  
Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN (Feed-Forward Neural Network) with 80% accuracy), because sharing  ...  Then, in the abnormal behavior detection and filtering component, the interesting profiles are selected for further examinations.  ...  Business Competitiveness of Castilla y León, and the European Regional Development Fund (FEDER).  ... 
doi:10.3390/electronics10243081 fatcat:jfdzljrwyncphijwyd5ipbuhqi

Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data [article]

Fahad AlGhamdi, Mona Diab
2019 arXiv   pre-print
We explore leveraging multiple neural network architectures to measure the impact of different pre-trained embeddings methods on POS tagging CS data.  ...  We investigate the landscape in four CS language pairs, Spanish-English, Hindi-English, Modern Standard Arabic- Egyptian Arabic dialect (MSA-EGY), and Modern Standard Arabic- Levantine Arabic dialect (  ...  Acknowledgments We would like to thank the four anonymous reviewers for their valuable comments and suggestions.  ... 
arXiv:1905.13359v1 fatcat:g4wgma74cjaxhg6yq2lrdolqae

Author Profiling in Informal and Formal Language Scenarios Via Transfer Learning

Daniel Escobar-Grisales, Juan Camilo Vásquez-Correa, Juan Rafael Orozco-Arroyave
2021 Tecno Lógicas  
This paper proposes the use of recurrent and convolutional neural networks and a transfer learning strategy to recognize two demographic traits, i.e., gender and language variety, in documents written  ...  The models were tested in two different databases consisting of tweets (informal) and call-center conversations (formal).  ...  Juan Rafael Orozco-Arroyave: funding acquisition, study design, writing, review, and critique. All the authors take responsibility for the integrity of the data and the accuracy of the data analysis.  ... 
doi:10.22430/22565337.2166 fatcat:kcgz5khd25fv3p43543sdyxj7u

Improving the classification of flood tweets with contextual hydrological information in a multimodal neural network

Jens A. de Bruijn, Hans de Moel, Albrecht H. Weerts, Marleen C. de Ruiter, Erkan Basar, Dirk Eilander, Jeroen C.J.H. Aerts
2020 Computers & Geosciences  
Improving the classification of flood tweets with contextual hydrological information in a multimodal neural network, Computers and Geosciences (2020), doi: https://doi.  ...  The classification data was obtained from Twitter using flood-related keywords in English, 16 French, Spanish and Indonesian.  ...  Most (little-known) locations are 147 poorly represented in pre-trained word embeddings, especially when a specific language is not spoken 148 in that location.  ... 
doi:10.1016/j.cageo.2020.104485 fatcat:7d7zsb4iqvawfnxkk6wytgyqd4

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

Ankur Dumka, Parag Verma, Rajesh Singh, Anil Kumar Bisht, Divya Anand, Hani Moaiteq Aljahdali, Irene Delgado Noya, Silvia Aparicio Obregon
2022 Computers Materials & Continua  
The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown.  ...  The evaluation shows interesting results in positive (1), negative (-1), and neutral (0) emotions through different visualization.  ...  Function embedding is an embedding layer of the CNN model that is used to initialize random weights and that learns embedding from all words in the training dataset.  ... 
doi:10.32604/cmc.2022.024698 fatcat:f4xe4xvwsnhpfjea7qdjjs6tdm

Hate versus Politics: Detection of Hate against Policy makers in Italian tweets [article]

Armend Duzha, Cristiano Casadei, Michael Tosi, Fabio Celli
2021 arXiv   pre-print
Finally, we visualized networks of hashtags to capture the topics used in hateful and normal tweets.  ...  Unfortunately, the amount of labelled data necessary for training models to detect hate speech are limited and domain-dependent.  ...  For more information, please get in touch with fabio.celli@maggioli.it.  ... 
arXiv:2107.05357v1 fatcat:scahjz7dnrcjlnboesh5tgu62a

Weakly supervised framework for aspect-based sentiment analysis on students' reviews of MOOCs

Zenun Kastrati, Ali Shariq Imran, Arianit Kurti
2020 IEEE Access  
Specifically, the framework relies on aspect-level sentiment analysis and aims to automatically identify sentiment or opinion polarity expressed towards a given aspect related to the MOOC.  ...  While this task may be viable for small-scale courses that involve just a few students' feedback, it is unpractical for large-scale cases as it applies to online courses in general, and MOOCs, in particular  ...  ACKNOWLEDGMENT The authors would like to thank the article [21] for providing us with the manually labeled dataset to carry out the work deemed necessary for comparison and evaluation of our framework  ... 
doi:10.1109/access.2020.3000739 fatcat:trl4ijilvzfj3ibr67m2jen5o4

Survey of Generative Methods for Social Media Analysis [article]

Stan Matwin, Aristides Milios, Paweł Prałat, Amilcar Soares, François Théberge
2021 arXiv   pre-print
We included two important aspects that currently gain importance in mining and modeling social media: dynamics and networks.  ...  This survey draws a broad-stroke, panoramic picture of the State of the Art (SoTA) of the research in generative methods for the analysis of social media data.  ...  Use of Pre-trained Embeddings for Neural Topic Modelling Recently the use of pre-trained embeddings leading to SoTA results for many NLP tasks has prompted researchers to begin to look for ways to incorporate  ... 
arXiv:2112.07041v1 fatcat:xgmduwctpbddfo67y6ack5s2um

Using a Hybrid-Classification Method to Analyze Twitter Data During Critical Events

Ahmed M. Khedr, Saadat M. Alhashmi, Ifra Arif, Magdi El Bannany
2021 IEEE Access  
A work presented in [7] used Bayesian Network Classifiers for sentiment analysis on two Spanish datasets: the Chilean earthquake (2010) and the Catalan independence referendum (2017).  ...  Word2Vec is one of the most popular technique to learn word embeddings using shallow neural network. Word embedding is one of the most popular representation of document vocabulary.  ... 
doi:10.1109/access.2021.3119063 fatcat:je3d3l2mcjcjpmjehrmeoxfjgy

A Deep Language-independent Network to analyze the impact of COVID-19 on the World via Sentiment Analysis [article]

Ashima Yadav, Dinesh Kumar Vishwakarma
2020 arXiv   pre-print
Also, attention-weights visualization and in-depth results analysis shows that the proposed network has effectively captured the sentiments of the people.  ...  We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net), which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanism  ...  The learned vector of [1* 100] dimension is passed to the dropout layer with a 0.5 rate to handle the overfitting problem in deep neural network architectures.  ... 
arXiv:2011.10358v1 fatcat:xcmffxjkijh2lmbt5rndcf2fvy

Using artificial intelligence techniques for detecting Covid-19 epidemic fake news in Moroccan tweets

Youness Madani, Mohammed Erritali, Belaid Bouikhalene
2021 Results in Physics  
We also demonstrate that the sentiment of tweets plays an important role in the detection of fake news.  ...  The present case study focuses on fake news being tweeted during the coronavirus pandemic for the purpose to mislead the targeted population.  ...  to influence the work reported in this paper.  ... 
doi:10.1016/j.rinp.2021.104266 fatcat:xxd6maxmvje4xoph34il3m7kfe

A Deep Learning Framework for Detection of COVID-19 Fake News on Social Media Platforms

Yahya Tashtoush, Balqis Alrababah, Omar Darwish, Majdi Maabreh, Nasser Alsaedi
2022 Data  
These deep neural networks have been trained and tested using the "COVID-19 Fake News" dataset, which contains 21,379 real and fake news instances for the COVID-19 pandemic and its vaccines.  ...  In this study, we investigate the ability of deep neural networks, namely, Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Network (CNN), and a hybrid of CNN and LSTM networks,  ...  Data Availability Statement: Data used in this article must be approved by the corresponding author. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/data7050065 fatcat:vss5bmv32rcdnbtbrgm6xxchdm
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