Applicability Analysis and Ensemble Application of BERT with TF-IDF, TextRank, MMR, and LDA for Topic Classification Based on Flood-Related VGI

Du, Wenying; Ge, Cui; Yao, Shuang; Chen, Nengcheng; Xu, Li

doi:10.3390/ijgi12060240

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…TF-IDF is a commonly applied approach that assigns a weight to each term in a document based on its frequency within the document and inverse frequency across all documents. These resulting weights indicate the significance of each term in the document and serve as features for the machine learning algorithms ( Du et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

El Koshiry,

Eliwa,

Abd El-Hafeez

et al. 2024

PeerJ Computer Science

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This study investigates the effectiveness of various deep learning and classical machine learning techniques in identifying instances of cyberbullying. The study compares the performance of five classical machine learning algorithms and three deep learning models. The data undergoes pre-processing, including text cleaning, tokenization, stemming, and stop word removal. The experiment uses accuracy, precision, recall, and F1 score metrics to evaluate the performance of the algorithms on the dataset. The results show that the proposed technique achieves high accuracy, precision, and F1 score values, with the Focal Loss algorithm achieving the highest accuracy of 99% and the highest precision of 86.72%. However, the recall values were relatively low for most algorithms, indicating that they struggled to identify all relevant data. Additionally, the study proposes a technique using a convolutional neural network with a bidirectional long short-term memory layer, trained on a pre-processed dataset of tweets using GloVe word embeddings and the focal loss function. The model achieved high accuracy, precision, and F1 score values, with the GRU algorithm achieving the highest accuracy of 97.0% and the NB algorithm achieving the highest precision of 96.6%.

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Section: Methodsmentioning

confidence: 99%

Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

El Koshiry,

Eliwa,

Abd El-Hafeez

et al. 2024

PeerJ Computer Science

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show abstract

“…This classification includes [28,33,34,[46][47][48], which are research works aimed at summarizing and retrieving crisis-relevant information from social media data to enhance situational awareness and decision making. Table 6 presents this detailed categorization scheme (Detailed in Appendix A, Table A3).…”

Section: Information Summarization and Retrievalmentioning

confidence: 99%

“…Other (e.g., SVM, TF-IDF, TextRank, LDA) [33,48] Effective summarization and topic classification in specific disaster-related contexts.…”

Section: Information Summarization and Retrievalmentioning

confidence: 99%

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

Sufi

2024

Sustainability

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Transformer technologies, like generative pre-trained transformers (GPTs) and bidirectional encoder representations from transformers (BERT) are increasingly utilized for understanding diverse social media content. Despite their popularity, there is a notable absence of a systematic literature review on their application in disaster analytics. This study investigates the utilization of transformer-based technology in analyzing social media data for disaster and emergency crisis events. Leveraging a systematic review methodology, 114 related works were collated from popular databases like Web of Science and Scopus. After deduplication and following the exclusion criteria, 53 scholarly articles were analyzed, revealing insights into the geographical distribution of research efforts, trends in publication output over time, publication venues, primary research domains, and prevalently used technology. The results show a significant increase in publications since 2020, with a predominant focus on computer science, followed by engineering and decision sciences. The results emphasize that within the realm of social-media-based disaster analytics, BERT was utilized in 29 papers, BERT-based methods were employed in 28 papers, and GPT-based approaches were featured in 4 papers, indicating their predominant usage in the field. Additionally, this study presents a novel classification scheme consisting of 10 distinct categories that thoroughly categorize all existing scholarly works on disaster monitoring. However, the study acknowledges limitations related to sycophantic behavior and hallucinations in GPT-based systems and raises ethical considerations and privacy concerns associated with the use of social media data. To address these issues, it proposes strategies for enhancing model robustness, refining data validation techniques, and integrating human oversight mechanisms.

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Deep-KeywordNet: automated english keyword extraction in documents using deep keyword network based ranking

Khatun,

Sarkar

2024

Multimed Tools Appl

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Applicability Analysis and Ensemble Application of BERT with TF-IDF, TextRank, MMR, and LDA for Topic Classification Based on Flood-Related VGI

Cited by 6 publications

References 48 publications

Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

Deep-KeywordNet: automated english keyword extraction in documents using deep keyword network based ranking

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