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Table 3 Performance metric of LR models using various features

From: Automated doubt identification from informal reflections through hybrid sentic patterns and machine learning approach

Model Features* Precision Recall F1 score
1 All data unigram features with resampling 0.61 0.46 0.52
2 All data unigram and bigram features with resampling 0.64 0.38 0.47
3 QM and 5W1H with resampling 0.29 0.38 0.33
4 QM, 5W1H and QP with resampling 0.29 0.38 0.33
5 TextBlob polarity score 0.24 0.51 0.33
6 Selected data with unigram features 0.70 0.67 0.68
7 Selected data with unigram, bigram features 0.83 0.62 0.71
8 Selected data with doc2vec embedding 0.76 0.75 0.75
  1. *The result of Models 1–7 is based on the TF as the feature vector space. The result from TF-IDF is omitted since it is consistently lower than the above