A Data-driven Affective Text Classification Analysis

Keyphrases

• BERT Model 33%
• Classification Accuracy 33%
• Classification Analysis 100%
• Classification Decision 33%
• Classification Model 100%
• Classification Prediction 33%
• Data Preprocessing 33%
• Dataset Size 33%
• Deep Learning 33%
• Emoji 66%
• Experiment Planning 33%
• Feature Classification 33%
• Feature Extraction Methods 33%
• Feature Extraction Model 33%
• Financial Economics 33%
• Logistic Regression 33%
• Misspellings 66%
• Model Accuracy 33%
• Naïve Bayes 33%
• NLP. 33%
• Pipeline Model 33%
• Processing Time 33%
• Sentiment Analysis 66%
• Sentiment Classification 33%
• Slang 33%
• Small Media 33%
• Term Frequency-inverse Document Frequency (TF-IDF) 33%
• Text Classification 100%
• Text Dataset 33%
• Textual Datasets 33%
• Training Model 33%
• Word Correction 33%
• Word2vec 33%

Computer Science

• Bidirectional Encoder Representations From Transformers 33%
• Classification Accuracy 33%
• Classification Analysis 100%
• Classification Models 100%
• Data Preprocessing 33%
• Decision-Making 33%
• Deep Learning 33%
• Feature Extraction 66%
• Logistic Regression 33%
• Model Accuracy 33%
• Processing Time 33%
• Sentiment Analysis 66%
• Sentiment Classification 33%
• Support Vector Machine 33%
• Text Classification 100%