A multi-label text classification model based on ELMo and attention

Liu, Wenbin; Wen, Bo; Gao, Shang; Zheng, Jiesheng; Zheng, Yinlong

doi:10.1051/matecconf/202030903015

Cited by 15 publications

(6 citation statements)

References 5 publications

(5 reference statements)

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“…The authors of this research proposed a model based on ELMo, attention mechanism and Softmax classifier. By this model, the appropriate label was predicted for each comment (Liu et al. , 2020).…”

Section: Related Workmentioning

confidence: 99%

Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining

Reisinezhad,

Fakhrahmad

2023

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PurposeQuestionnaire studies of knowledge, attitude and practice (KAP) are effective research in the field of health, which have many shortcomings. The purpose of this research is to propose an automatic questionnaire-free method based on deep learning techniques to address the shortcomings of common methods. Next, the aim of this research is to use the proposed method with public comments on Twitter to get the gaps in KAP of people regarding COVID-19.Design/methodology/approachIn this paper, two models are proposed to achieve the mentioned purposes, the first one for attitude and the other for people’s knowledge and practice. First, the authors collect some tweets from Twitter and label them. After that, the authors preprocess the collected textual data. Then, the text representation vector for each tweet is extracted using BERT-BiGRU or XLNet-GRU. Finally, for the knowledge and practice problem, a multi-label classifier with 16 classes representing health guidelines is proposed. Also, for the attitude problem, a multi-class classifier with three classes (positive, negative and neutral) is proposed.FindingsLabeling quality has a direct relationship with the performance of the final model, the authors calculated the inter-rater reliability using the Krippendorf alpha coefficient, which shows the reliability of the assessment in both problems. In the problem of knowledge and practice, 87% and in the problem of people’s attitude, 95% agreement was reached. The high agreement obtained indicates the reliability of the dataset and warrants the assessment. The proposed models in both problems were evaluated with some metrics, which shows that both proposed models perform better than the common methods. Our analyses for KAP are more efficient than questionnaire methods. Our method has solved many shortcomings of questionnaires, the most important of which is increasing the speed of evaluation, increasing the studied population and receiving reliable opinions to get accurate results.Research limitations/implicationsOur research is based on social network datasets. This data cannot provide the possibility to discover the public information of users definitively. Addressing this limitation can have a lot of complexity and little certainty, so in this research, the authors presented our final analysis independent of the public information of users.Practical implicationsCombining recurrent neural networks with methods based on the attention mechanism improves the performance of the model and solves the need for large training data. Also, using these methods is effective in the process of improving the implementation of KAP research and eliminating its shortcomings. These results can be used in other text processing tasks and cause their improvement. The results of the analysis on the attitude, practice and knowledge of people regarding the health guidelines lead to the effective planning and implementation of health decisions and interventions and required training by health institutions. The results of this research show the effective relationship between attitude, practice and knowledge. People are better at following health guidelines than being aware of COVID-19. Despite many tensions during the epidemic, most people still discuss the issue with a positive attitude.Originality/valueTo the best of our knowledge, so far, no text processing-based method has been proposed to perform KAP research. Also, our method benefits from the most valuable data of today’s era (i.e. social networks), which is the expression of people’s experiences, facts and free opinions. Therefore, our final analysis provides more realistic results.

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Section: Related Workmentioning

confidence: 99%

Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining

Reisinezhad,

Fakhrahmad

2023

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“…At present, the classical classification algorithms are naive Bayes classifier (NBC), support-vector machine, association rules, decision tree (DT), K-nearest neighbor, genetic algorithm, neural network, and so on [10][11][12][13].…”

Section: Research Status Of Single-label Classificationmentioning

confidence: 99%

Research on Model and Algorithm of Multiview and Multilabel Classification Based on Nearest-Neighbor Model

Zhang

Rao

2022

Mathematical Problems in Engineering

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In the field of machine learning, multilabel learning gradually evolved from the traditional text classification problem. In this study, a multiview multilabel via optimal classifier chain (MVMLOCC) algorithm based on the nearest-neighbor model is proposed. The algorithm model establishes a multilabel chain classifier for each view of the dataset and predicts unknown data samples by dynamically adjusting the weights of chain learners. When an unknown example is input, multiple chain classifier models are multiplied by corresponding weights to obtain the final tag set situation. The model makes full use of the relevance between multiple tags and the complementarity and integrity between multiple perspectives and can achieve better learning results. The final experimental results show that our proposed model can be applied to different multilabel classification tasks and has achieved excellent performance under different evaluation indicators. This study mainly studies the multiview and multilabel classification method and uses active learning technology to solve the problem of high label components in data collection.

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“…Saat ini, terdapat beragam pendekatan yang dilakukan oleh para peneliti dalam mengembangkan metode klasifikasi teks multi label pada research article, diantaranya adalah dengan pendekatan klasifikasi ensemble k-nearest (Wu, Han, Chen, Li, & Zhang, 2022) , multilayer neural network (Liu, Wen, Gao, Zheng, & Zheng, 2020), graph neural network (Pal, Selvakumar, & Sankarasubbu, 2020) Penelitian ini melakukan klasifikasi multi label pada teks toxic comments menggunakan pedekatan ensemble classifier yaitu dengan mengkombinasikan banyak basis pengklasifikasi. Pendekatan ensemble telah terbukti mampu meningkatkan performa klasifikasi pengkalsifikasi tunggal Terdapat tiga metode utama pada ensemble classifier, yaitu teknik bagging, teknik boosting, dan teknik stacking.…”

Section: Penelitian Terkaitunclassified

Text Mining Dan Klasifikasi Multi Label Menggunakan Xgboost

Siringoringo

Jamaluddin

Perangin-angin

2022

jmika

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The conventional classification process is applied to find a single criterion or label. The multi-label classification process is more complex because a large number of labels results in more classes. Another aspect that must be considered in multi-label classification is the existence of mutual dependencies between data labels. In traditional binary classification, classification analysis only aims to determine the label in the text, whether positive or negative. This method is sub-optimal because the relationship between labels cannot be determined. To overcome the weaknesses of these traditional methods, multi-label classification is one of the solutions in data labeling. With multi-label text classification, it allows the existence of many labels in a document and there is a semantic correlation between these labels. This research performs multi-label classification on research article texts using the ensemble classifier approach, namely XGBoost. Classification performance evaluation is based on several metrics criteria of confusion matrix, accuracy, and f1 score. Model evaluation is also carried out by comparing the performance of XGBoost with Logistic Regression. The results of the study using the train test split and cross-validation obtained an average accuracy of training and testing for Regression Logistics of 0.81, and an average f1 score of 0.47. The average accuracy for XGBoost is 0.88, and the average f1 score is 0.78. The results show that the XGBoost classifier model can be applied to produce a good classification performance.

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A multi-label text classification model based on ELMo and attention

Cited by 15 publications

References 5 publications

Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining

Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining

Research on Model and Algorithm of Multiview and Multilabel Classification Based on Nearest-Neighbor Model

Text Mining Dan Klasifikasi Multi Label Menggunakan Xgboost

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