Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification

Sharma, Tanu; Kaur, Kamaldeep

doi:10.3390/app112210542

Cited by 15 publications

(9 citation statements)

References 44 publications

(59 reference statements)

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“…Here, evaluation parameters such as macro precision (macro-P), macro recall (macro-R), and macro F1 (macro-F1) are introduced to determine the performance of the entire model. The formulas for calculating the above evaluation parameters are as follows (Sharma and Kaur 2021):…”

Section: Model Performance Evaluation Metricsmentioning

confidence: 99%

Study on deep learning methods for rockburst risk prediction based on mining-induced seismicity quantification

Cheng

Qiao

2023

Preprint

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The assessment of rockburst risk (RBR) is the premise of rockburst prevention and control. It is the implementation criterion to guide various rockburst prevention and control measures. The existing static prediction and evaluation methods for RBR cannot be effectively combined with the results of underground dynamic monitoring. This study proposed a mining-induced seismicity information quantification method based on the fractal theory. Deep learning methods are used to construct a deep learning framework of rockburst risk (DLFR) based on the fractal dimension of microseismic information. Gray correlation analysis (GRA), information gain ratio (IGR), and Pearson correlation coefficient are used to screen and compare factors. Statistical evaluation indicators such as macro-F1, accuracy rate, and fitness curve were used to evaluate model performance. Taking Gaojiapu Coal Mine in the Binchang mining area as a case study, the performance of deep learning models such as BP Neural Network (BP), Support Vector Machine (SVM) and its optimized model based on particle swarm optimization (PSO) algorithm under this framework is discussed. The reliability and validity of the research results to verify by comparing the predicted results with the actual results. The research results show that the prediction results of RBR in DLFR are consistent with the actual results, and the model is reliable and effective. The mining-induced seismicity quantification can solve the problem of insufficient training samples for the RBR. With this, different pressure relief measures can be formulated based on the results of the RBR predictions to achieve "graded" precise prevention and control.

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Section: Model Performance Evaluation Metricsmentioning

confidence: 99%

Study on deep learning methods for rockburst risk prediction based on mining-induced seismicity quantification

Cheng

Qiao

2023

Preprint

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“…Here, evaluation parameters such as macro precision (macro-P), macro recall (macro-R), and macro F1 (macro-F 1 ) are introduced to determine the performance of the entire model. The formulas for calculating the above evaluation parameters are as follows (Sharma and Kaur 2021):…”

Section: Model Performance Evaluation Metricsmentioning

confidence: 99%

Study on deep learning methods for coal burst risk prediction based on mining-induced seismicity quantification

Cheng,

Qiao,

2023

Geomech. Geophys. Geo-energ. Geo-resour.

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The assessment of Coal burst risk (CBR) is the premise of bump disaster prevention and control. It is the implementation criterion to guide various rock burst prevention and control measures. The existing static prediction and evaluation methods for CBR cannot be effectively combined with the results of underground dynamic monitoring. This study proposed a mining-induced seismicity information quantification method based on the fractal theory. Deep learning methods were used to construct a deep learning framework of coal burst risk (DLFR) based on the fractal dimension of microseismic information. Gray correlation analysis (GRA), information gain ratio (IGR), and Pearson correlation coefficient are used to screen and compare factors. Statistical evaluation indicators such as macro-F1, accuracy rate, and fitness curve were used to evaluate model performance. Taking the Gaojiapu coal mine as a case study, the performance of deep learning models such as BP Neural Network (BP), Support Vector Machine (SVM) and its optimized model based on particle swarm optimization (PSO) algorithm under this framework is discussed. The research results' reliability and validity are verified by comparing the predicted results with the actual results. The research results show that the prediction results of CBR in DLFR are consistent with the actual results, and the model is reliable and effective. The mining-induced seismicity quantification can solve the problem of insufficient training samples for the CBR. With this, different pressure relief measures can be formulated based on the results of the CBR predictions to achieve "graded" precise prevention and control.

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“…Traditional machine learning (as implemented in [58,59]) and deep learning [60][61][62] classifiers have already been applied to the sentiment analysis problem. Recently, deep learning methods were combined with ensemble learning [63].…”

Section: Single-model Machine Learning Classifiersmentioning

confidence: 99%

Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning

2022

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We live in a digitized era where our daily life depends on using online resources. Businesses consider the opinions of their customers, while people rely on the reviews/comments of other users before buying specific products or services. These reviews/comments are usually provided in the non-normative natural language within different contexts and domains (in social media, forums, news, blogs, etc.). Sentiment classification plays an important role in analyzing such texts collected from users by assigning positive, negative, and sometimes neutral sentiment values to each of them. Moreover, these texts typically contain many expressed or hidden emotions (such as happiness, sadness, etc.) that could contribute significantly to identifying sentiments. We address the emotion detection problem as part of the sentiment analysis task and propose a two-stage emotion detection methodology. The first stage is the unsupervised zero-shot learning model based on a sentence transformer returning the probabilities for subsets of 34 emotions (anger, sadness, disgust, fear, joy, happiness, admiration, affection, anguish, caution, confusion, desire, disappointment, attraction, envy, excitement, grief, hope, horror, joy, love, loneliness, pleasure, fear, generosity, rage, relief, satisfaction, sorrow, wonder, sympathy, shame, terror, and panic). The output of the zero-shot model is used as an input for the second stage, which trains the machine learning classifier on the sentiment labels in a supervised manner using ensemble learning. The proposed hybrid semi-supervised method achieves the highest accuracy of 87.3% on the English SemEval 2017 dataset.

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Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification

Cited by 15 publications

References 44 publications

Study on deep learning methods for rockburst risk prediction based on mining-induced seismicity quantification

Study on deep learning methods for rockburst risk prediction based on mining-induced seismicity quantification

Study on deep learning methods for coal burst risk prediction based on mining-induced seismicity quantification

Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning

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