Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model
Abstract
:1. Introduction
2. Literature Survey
3. Methodology
Dataset
4. Compute Unified Device Architecture (CUDA) and Programming
GF108 Architecture
5. Proposed Method
5.1. Making Predictions with Logistic Regression
5.2. Decision Tree
5.2.1. Entropy
5.2.2. Information Gain
- Find the entropy of the target attribute.
- Entropy of every branch is calculated to find the best split.
- Select attribute with high info gain and recursively repeat the same for all the remaining branches.
- If entropy is zero, then consider all of them as a leaf node; else, continue splitting.
- Repeat all the above steps until data is classified.
5.3. Proposed Compute Unified Device Architecture (CUDA) Sentimental Analysis Database Miner Classifier
Algorithm 1: Decision Tree Building Algorithm |
Algorithm 2: Accuracy Prediction Algorithm |
Call: xgboost() M= x.xgbClassifier() m.fit(X_train,Y_train) Call: ConstructTree() c= confusion_matrix (Y_test,Y_pred) Calculate: Accuracy = Number of correct predictions/total predictions Return accuracy Stop |
6. Implementation and Results
6.1. Acceleration Ratio
6.2. Performance Evaluation Measures
7. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Author | Algorithms Used | Feature-Selection | Data Source | Accuracy |
---|---|---|---|---|
Gualtiero Bcolombo (2015) | Graph mining | TF-IDF | Web forums(Twitter Data) | 84% |
Dmytro Karamshuk (2017) | Glove word vector, Conventional classification, DT | Consistency Label | Public Twitter | 85% |
Tong Liu (2017) | Support vector Machine (SVM) | TF-IDF, N-Grams | Historical Twitters posts | 88% |
Bridianne O’Dea (2015) | SVM, Logistic regression | TF-IDF wih filter and without filter and no filter, Data points | CSIRO | 80% |
Pete Burnap (2015) | SVM, Naïve Bayes (NB), Decision Tree (DT), Rotation forest | Lexical, Structural, Emotive, Psychological TF-IDF, N-Grams, | Web forums (Twitter Data) | 75% |
Benjamin. L (2016) | Logistic regression | N-Grams, Linguistic context | Kaggle | 82% |
Mia Johnson Vioules (2017) | NB, Sequential minimal optimization (SMO), Decision tree (J48), | NBB, Multinomial L-R, RF | 80% | |
Scott R Braithwaite (2016) | Decision Tree (DT) | Linguistic, word count | Amazon Mechanical Turk (AMT) | 76% |
Munmum De Choudhury (2013) | SVM with a radial-basis function (RBF) kernel | Depression set | Crowdsourcing | 86% |
Ramit Sawhney (2018) | Ensemble, Linear classification | Twitter streaming API | 81% | |
Bart Desmet (2018) | Parallel Computing | Bag of words, polarity lexicon | KAGGLE | 92% |
Shaoxiong Ji (2018) | SVM, random forest, gradient boost classification, XGboost | TF-IDF, semantics and syntactic, statistics, Linguistic features | Reddit and Twitter blogs | 89% |
Jingcheng Du (2018) | CNN binary classification | Linguistic features | Twitter streaming API | 74% |
No of Threads | Time Taken |
---|---|
128 | 5.12 |
256 | 4.14 |
512 | 3.12 |
1024 | 2.69 |
SADBM GPU Time | No of | No of | No of | No of | No of |
---|---|---|---|---|---|
Records | Records | Records | Records | Records | |
s/12,000 | s/32,000 | s/52,000 | s/72,000 | s/92,000 | |
Classification Time | 0.552 | 1.020 | 1.705 | 2.052 | 2.742 |
CPU-Time | 0.710 | 1.130 | 1.740 | 2.3500 | 2.900 |
GPU-Time | 0.550 | 1.010 | 1.640 | 2.230 | 2.490 |
Acceleration Ratio | 1.296 | 1.118 | 1.064 | 1.054 | 1.1649 |
Polarity | Precision | Recall | F-Score | Support |
---|---|---|---|---|
0 | 0.79 | 0.82 | 0.77 | 2467 |
1 | 0.884 | 0.81 | 0.84 | 5765 |
Accuracy | 0.81 | 8232 | ||
MacroAvg | 0.81 | 0.82 | 0.81 | 8232 |
WeightedAvg | 0.81 | 0.81 | 0.81 | 8232 |
Polarity | Precision | Recall | F-Score | Support |
---|---|---|---|---|
0 | 0.89 | 0.84 | 0.88 | 2899 |
1 | 0.884 | 0.91 | 0.89 | 5333 |
Accuracy | 0.89 | 8232 | ||
MacroAvg | 0.87 | 0.88 | 0.87 | 8232 |
WeightedAvg | 0.89 | 0.89 | 0.89 | 8232 |
Polarity | Precision | Recall | F-Score | Support |
---|---|---|---|---|
0 | 0.953 | 0.943 | 0.924 | 2882 |
1 | 0.950 | 0.948 | 0.946 | 5350 |
Accuracy | 0.96 | 8232 | ||
MacroAvg | 0.96 | 0.96 | 0.956 | 8232 |
WeightedAvg | 0.955 | 0.961 | 0.959 | 8232 |
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Pathuri, S.K.; Anbazhagan, N.; Joshi, G.P.; You, J. Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model. Sensors 2022, 22, 80. https://doi.org/10.3390/s22010080
Pathuri SK, Anbazhagan N, Joshi GP, You J. Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model. Sensors. 2022; 22(1):80. https://doi.org/10.3390/s22010080
Chicago/Turabian StylePathuri, Siva Kumar, N. Anbazhagan, Gyanendra Prasad Joshi, and Jinsang You. 2022. "Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model" Sensors 22, no. 1: 80. https://doi.org/10.3390/s22010080