Aspect-based-Sentiment-Analysis

Implementation of some aspect-based sentiment analysis models; based on car dataset/ laptop dataset; 3 classes

Models

• ATAE-LSTM(Attention-based LSTM with Aspect Embedding)
  Attention-based LSTM for Aspect-level Sentiment Classification

• TSA(A Siamese Bidirectional LSTM with context-aware attention) DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis

Data Analysis

item	value
training set	12813
valid/dev set	1602
test set	1602
char_vocab	2378
all_char_vocab	2379
aspect	20
aspect_text_char_vocab	69
char_max_len	127
< char_len = 0.991	110
aspect_text_char_max_len	19
< aspect_text_char_len = 0.978	18

outputs(performance)

• random

model	acc(on test)	acc(on dev)	macro-f1(test)	macro-f1(dev)
atae_lstm	0.6536	0.6685	0.5952	0.6143
tsa	0.6654	0.6816	0.6194	0.6381

• word2vector

model	acc(on test)	acc(on dev)	macro-f1(test)	macro-f1(dev)
atae_lstm	0.6704	0.6685	0.6351	0.6347
tsa	0.6710	0.6792	0.6296	0.6445

• glove

model	acc(on test)	acc(on dev)	macro-f1(test)	macro-f1(dev)
atae_lstm	0.6461	0.6754	0.5863	0.6226
tsa	0.6567	0.6798	0.6198	0.6461

screenshots --> outputs file

str tree(run codes)

.
├── ckpt
|   ├── car    saved model files
|   ├── laptop
|   └── others
├── data
│   ├── car    preprocessed data files
|   ├── laptop
|   └── others
├── xxx.csv    csv files of pre & rl labels
├── config.py
├── data_loader.py
├── layers.py
├── models.py
├── preprocess.py
├── train.py
└── utils.py

car : char level / laptop : word level