HAABSA

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Code for A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models

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# HAABSA
Code for A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models

All software is written in PYTHON3 (https://www.python.org/) and makes use of the TensorFlow framework (https://www.tensorflow.org/).

## Installation Instructions (Windows):
### Dowload required files and add them to data/externalData folder:
1. Download ontology: https://github.com/KSchouten/Heracles/tree/master/src/main/resources/externalData
2. Download SemEval2015 Datasets: http://alt.qcri.org/semeval2015/task12/index.php?id=data-and-tools
3. Download SemEval2016 Dataset: http://alt.qcri.org/semeval2016/task5/index.php?id=data-and-tools
4. Download Glove Embeddings: http://nlp.stanford.edu/data/glove.42B.300d.zip
5. Download Stanford CoreNLP parser: https://nlp.stanford.edu/software/stanford-parser-full-2018-02-27.zip
6. Download Stanford CoreNLP Language models: https://nlp.stanford.edu/software/stanford-english-corenlp-2018-02-27-models.jar

### Setup Environment
1. Install chocolatey (a package manager for Windows): https://chocolatey.org/install
2. Open a command prompt.
3. Install python3 by running the following command: `code(choco install python)` (http://docs.python-guide.org/en/latest/starting/install3/win/).
4. Make sure that pip is installed and use pip to install the following packages: setuptools and virtualenv (http://docs.python-guide.org/en/latest/dev/virtualenvs/#virtualenvironments-ref).
5. Create a virtual environemnt in a desired location by running the following command: `code(virtualenv ENV_NAME)`
6. Direct to the virtual environment source directory. 
7. Unzip the HAABSA_software.zip file in the virtual environment directrory. 
8. Activate the virtual environment by the following command: 'code(Scripts\activate.bat)`.
9. Install the required packages from the requirements.txt file by running the following command: `code(pip install -r requirements.txt)`.
10. Install the required space language pack by running the following command: `code(python -m spacy download en)`

### Run Software
1. Configure one of the three main files to the required configuration (main.py, main_cross.py, main_hyper.py)
2. Run the program from the command line by the following command: `code(python PROGRAM_TO_RUN.py)` (where PROGRAM_TO_RUN is main/main_cross/main_hyper)

## Software explanation:
The environment contains the following main files that can be run: main.py, main_cross.py, main_hyper.py
- main.py: program to run single in-sample and out-of-sample valdition runs. Each method can be activated by setting its corresponding boolean to True e.g. to run the CABASC method set runCABASC = True.
- main_cross.py: similar to main.py but runs a 10-fold cross validation procedure for each method.
- main_hyper.py: program that is able to do hyperparameter optimzation for a given space of hyperparamters for each method. To change a method change the objective and space parameters in the run_a_trial() function.

- config.py: contains parameter configurations that can be changed such as: dataset_year, batch_size, iterations.

- dataReader2016.py, loadData.py: files used to read in the raw data and transform them to the required formats to be used by one of the algorithms

- lcrModel.py: Tensorflow implementation for the LCR-Rot algorithm
- lcrModelAlt.py: Tensorflow implementation for the LCR-Rot-hop algorithm
- lcrModelInverse.py: Tensorflow implementation for the LCR-Rot-inv algorithm
- cabascModel.py: Tensorflow implementation for the CABASC algorithm
- OntologyReasoner.py: PYTHON implementation for the ontology reasoner
- svmModel.py: PYTHON implementation for a BoW model using a SVM.

- att_layer.py, nn_layer.py, utils.py: programs that declare additional functions used by the machine learning algorithms.

## Directory explanation:
The following directories are necessary for the virtual environment setup: \__pycache, \Include, \Lib, \Scripts, \tcl, \venv
- cross_results_2015: Results for a k-fold cross validation process for the SemEval-2015 dataset
- cross_results_2016: Results for a k-fold cross validation process for the SemEval-2015 dataset
- data:
	- externalData: Location for the external data required by the methods
	- programGeneratedData: Location for preprocessed data that is generated by the programs
- hyper_results: Contains the stored results for hyperparameter optimzation for each method
- results: temporary store location for the hyperopt package

## Related Work: ##
This code uses ideas and code of the following related papers:
- Zheng, S. and Xia, R. (2018). Left-center-right separated neural network for aspect-based sentiment analysis with rotatory attention. arXiv preprint arXiv:1802.00892.
- Schouten, K. and Frasincar, F. (2018). Ontology-driven sentiment analysis of product and service aspects. In Proceedings of the 15th Extended Semantic Web Conference (ESWC 2018). Springer. To appear
- Liu, Q., Zhang, H., Zeng, Y., Huang, Z., and Wu, Z. (2018). Content attention model for aspect based sentiment analysis. In Proceedings of the 27th International World Wide Web Conference (WWW 2018). ACM Press.

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