A three-level classification of French tweets in ecological crises

Kozlowski, Diego; Saudemont, Frédéric; Benamara, Farah; Mari, Alda; Boumadane, Abdelmoumene

doi:10.1016/j.ipm.2020.102284

Cited by 15 publications

(7 citation statements)

References 28 publications

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“…Text categorization algorithms have been successfully applied to Korean/French/Arabic/Tigrinya/Chinese languages for document/tweets classification (Kozlowski et al 2020 ), (Jin et al 2020 ). CNN with the CBOW model achieves an accuracy of 93.41% for classifying text in the Trigniya language (Fesseha et al 2021 ).…”

Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

“… Alharthi et al ( 2021 ) Arabic text low-quality content classification Twitter dataset CNN, LSTM Word2Vec, AraVec LSTM achieves an accuracy of 98% 3. Kozlowski et al ( 2020 ) French social media tweet analysis for crisis management French dataset SVM, CNN fastText, BERT, French FlauBert FlauBert achieves a micro F1-score of 85.4% 4. Zuheros et al ( 2019 ) Social networking site tweet analysis for the use of polysemic words Social media texts, both English and Spanish data XGBoost, HAN, LSTM GloVe LSTM + GloVe achieves an F1-score of 97.90% 5.…”

Section: Appendix Amentioning

confidence: 99%

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Impact of word embedding models on text analytics in deep learning environment: a review

2023

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The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

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Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

Section: Appendix Amentioning

confidence: 99%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

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“…On the other hand, NLP has contributed to the generation of terminological sources for the classification and forecasting of rare events or crises [ 17 – 20 ]. For example, in [ 18 ], it is presented the first study for crisis management using French transformer-based architectures (BERT, FlauBERT, and CamemBERT) apply to French social media, to classify tweets for natural disasters. In [ 21 ], the authors make use of the Bayesian model averaging approach and linear-chain conditional random fields to extract knowledge from tweets and build a decision support system to identify early warning signs of earthquakes.…”

Section: Related Workmentioning

confidence: 99%

“… These two previous set of layers are generally followed by one or more fully connected layers As previously stated, CNN has been a game changer in the field of image analysis. They also have shown some interesting results in NLP [ 18 , 27 ]. Indeed, texts can be represented an array of vectors, just like images can be represented by an array of pixel values.…”

Section: Nlp Modelsmentioning

confidence: 99%

The usefulness of NLP techniques for predicting peaks in firefighter interventions due to rare events

Cerna

Guyeux

Laiymani

2022

Neural Comput & Applic

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In some countries such as France, the number of operations assisted by firefighters has shown an almost linear increase over the years, contrary to their resource capacity. For this reason, predicting the number of interventions has become a necessity. Initially, time series models were developed with several types of qualitative and quantitative features, including the alert level of the bulletins, to predict the operational load. We realized that interventions related to human activities are quite predictable. However, the recognition of interventions due to rare events such as storms or floods needs more than quantitative meteorological data to be identified, since there are almost always zero cases. Thus, this work proposes the application of natural language processing techniques, namely long short-term memory, convolutional neural networks, FlauBERT, and CamemBERT to extract features from the texts of weather bulletins in order to recognize periods with peak interventions, where the intense workload of firefighters is caused by rare events. Four categories identified as Emergency Person Rescue, Total Person Rescue, interventions related to Heating, and Storm/Flood were our targets for the multilabel classification models developed. The results showed a remarkable accuracy of 80%, 86%, 92%, and 86% for Emergency Rescue People, Total Rescue People, Heating, and Storm/Flood, respectively.

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“…Millions of social network users express and communicate their ideas through social media. Previous studies have demonstrated the value of internet data in such a big data age that they can be used in various aspects including crisis response and management ( Alkhodair et al, 2020 ; Imran et al, 2020 ; Jamali et al, 2019 ; Kaufhold et al, 2020 ; S. Li et al, 2020 ), commercial fields ( Kozlowski et al, 2020 ; Seki et al, 2022 ) like consumer attitudes and behaviors ( Chen & Zhang, 2022 ; Laguna et al, 2020 ), and political activities (Z. Li et al, 2022 ; Mohammad et al, 2015 ; Stamatelatos et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

An easy numeric data augmentation method for early-stage COVID-19 tweets exploration of participatory dynamics of public attention and news coverage

Yuan¹,

Zhang²

2022

Information Processing & Management

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A three-level classification of French tweets in ecological crises

Cited by 15 publications

References 28 publications

Impact of word embedding models on text analytics in deep learning environment: a review

Impact of word embedding models on text analytics in deep learning environment: a review

The usefulness of NLP techniques for predicting peaks in firefighter interventions due to rare events

An easy numeric data augmentation method for early-stage COVID-19 tweets exploration of participatory dynamics of public attention and news coverage

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