Aspect Based Sentiment Analysis
Paper
Dataset
ABSA-15_Restaurants_Train_Final.xml
Approach
Natural Language Processing based. Multilabel classifier on top of syntactic extraction rules.
Results
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 582/582 [02:18<00:00, 3.10it/s]
For Data-set: dataset/ABSA15_Restaurants_Test.json
precision recall f1-score support
0 0.00 0.00 0.00 54
1 0.83 0.50 0.63 542
avg / total 0.76 0.46 0.57 596
[root] [2017-09-27 17:59:03,966] INFO : Shape of array for dataset: X:(582, 13635) , Y:(582, 31)
/Users/Amit/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
Classification report on testing_data
precision recall f1-score support
0 0.97 0.93 0.95 101
1 1.00 1.00 1.00 2
2 1.00 0.54 0.70 54
3 1.00 1.00 1.00 2
4 1.00 1.00 1.00 3
5 1.00 1.00 1.00 2
6 1.00 0.74 0.85 23
7 1.00 1.00 1.00 1
8 0.00 0.00 0.00 1
9 1.00 0.83 0.91 6
10 1.00 0.33 0.50 3
11 1.00 1.00 1.00 1
12 1.00 1.00 1.00 14
13 0.94 0.79 0.86 19
14 1.00 1.00 1.00 1
15 0.00 0.00 0.00 1
16 1.00 1.00 1.00 2
17 1.00 1.00 1.00 5
avg / total 0.97 0.80 0.87 241
[root] [2017-09-27 17:59:19,624] INFO : Dataset: dataset/ABSA15_Restaurants_Test.json
582it [00:00, 628.15it/s]
NO PREDICTION FOR RULE: 397 out of: 582
Task: ONLY_ASPECT_PREDICTION False
Accuracy: 80.96885813148789
Total: 289 , Correct: 234
:::::::::::::::::: TESTING ::::::::::::::::::
dataset/ABSA15_Restaurants_Test.json
precision recall f1-score support
0 0.00 0.00 0.00 55
1 0.81 0.43 0.56 542
avg / total 0.74 0.39 0.51 597
Setup
# From project root, execute this command:
pip install -r requirements.txtCommands
# From project root, execute this command:
PYTHONPATH='.' python3 training/train_top_classifier.py