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

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.

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

Similar to Ma and Hovy.

• 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.

• 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

python xmlToIOB.py

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

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"

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

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.