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https://github.com/soujanyaporia/aspect-extraction

Aspect extraction from product reviews - window-CNN+maxpool+CRF, BiLSTM+CRF, MLP+CRF
https://github.com/soujanyaporia/aspect-extraction

aspect-based-sentiment-analysis aspect-extraction bilstm-crf cnn cnn-crf semeval2014 semeval2016 sentiment-analysis sequence-labeling tensorflow

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Aspect extraction from product reviews - window-CNN+maxpool+CRF, BiLSTM+CRF, MLP+CRF

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README 

        
# Aspect extraction from product reviews with Tensorflow



This repo has multiple sequential models for aspect extraction from product reviews.



## Citation



If the code is useful in your research, please cite the following paper:



Poria, S., Cambria, E. and Gelbukh, A., 2016. Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108, pp.42-49.



## Task



Given a sentence, the task is to extract aspects. Here is an example



```

I like the battery life of this phone"



Converting this sentence to IOB would look like this -



I O

like O

the O

battery B-A

life I-A

of O

this O

phone O



```



## Model



Similar to [Ma and Hovy](https://arxiv.org/pdf/1603.01354.pdf).



- concatenate final states of a bi-lstm on character embeddings to get a character-based representation of each word

- concatenate this representation to a standard word vector representation (GloVe here)

- run a bi-lstm on each sentence to extract contextual representation of each word

- decode with a linear chain CRF



Similar to [Collobert et al.] (http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf)



- form a window around the word to tag

- apply MLP on that window

- obtain logits

- apply viterbi (CRF) for sequence tagging



Similar to [Poria et al.](https://www.sciencedirect.com/science/article/pii/S0950705116301721)



- form a window around the word to tag

- apply CNN on that window

- apply maxpool on that window (Caution: different from global maxpool)

- obtain logits

- apply CRF for sequence tagging



## XML to IOB



```

python xmlToIOB.py

```



## Details



Download Glove embeddings (GloVe: http://nlp.stanford.edu/data/glove.840B.300d.zip )



1. [DO NOT MISS THIS STEP] Build vocab from the data and extract trimmed glove vectors according to the config in `model/config.py`.



```

python build_data.py

```



2. Train the model with



```

python train.py

```



3. Evaluate and interact with the model with

```

python evaluate.py

```



Data iterators and utils are in `model/data_utils.py` and the model with training/test procedures is in `model/aspect_model.py`



## Training Data



The training data must be in the following format (identical to the CoNLL2003 dataset).



A default test file is provided to help you getting started.



```

The	O

duck	B-A

confit	I-A

is	O

always	O

amazing	O

and	O

the	O

foie	B-A

gras	I-A

terrine	I-A

with	I-A

figs	I-A

was	O

out	O

of	O

this	O

world	O



The	O

wine	B-A

list	I-A

is	O

interesting	O

and	O

has	O

many	O

good	O

values	O

```



Once you have produced your data files, change the parameters in `config.py` like



```

# dataset

filename_train = "data/ABSA16_Restaurants_Train_SB1_v2_mod.iob"

filename_dev = "data/EN_REST_SB1_TEST_2016_mod.iob"

filename_test = "data/EN_REST_SB1_TEST_2016_mod.iob"

```



## Result



Chunk based evaluation



```

Laptop 2014 -> F1 - 79.93



Restaurant 2014 -> F1 - 84.22

```

Partial matching based evaluation

```

Laptop 2014 -> F1 - 86.84

Restaurant 2014 -> F1 - 88.42

```



## License



This project is licensed under the terms of the apache 2.0 license (as Tensorflow and derivatives). If used for research, citation would be appreciated.