ALDONA

Code for ALDONA: A Hybrid Solution for Sentence-Level Aspect Based Sentiment Analysis using a Lexicalized Domain Ontology and Neural Attention Model

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

Installation Instructions:

Download required files:

1. Download ontology: https://github.com/KSchouten/Heracles/tree/master/src/main/resources/externalData
2. Download SemEval2016 Dataset: http://alt.qcri.org/semeval2016/task5/index.php?id=data-and-tools
3. Download GloVe Embeddings: http://nlp.stanford.edu/data/glove.42B.300d.zip

Setup Environment

1. Download and install PYTHON3.

2. Install the required packages from the requirements.txt file by running the following command in a command line: pip install -r requirements.txt

3. Download and unzip ALDONA.zip

4. Place the ontology.owl in the Ontology folder

5. Place glove.42B.300d.txt in Data/Glove

6. Place EN_REST_SB1_TEST.xml.gold in Data/Test_data

7. Place ABSA16_Restaurants_Train_SB1_v2.xml in Data/Train_data

Run Software

1. Configure the main file to the required configuration (MAIN.py)
2. Run the program from the command line by the following command: python MAIN.py

Software explanation:

The environment contains the following main file that can be run: MAIN.py

• MAIN.py: the main program to choose data generation options, hyper-parameters and to run specified models
• MAIN_Data_Generator.py: the centralized program to read raw data and transform into the required formats. It incorporates the following programs:
  • Data_Processer.py: program to extract raw data and transform it into vectors
    • Data_Reader.py: program to extract raw data
  • GloVe.py: program to transform GloVe vectors to the required format and create a list of all available words
    • Word_Embeddings.py: transform data vectors into embeddings
    • Visualization.py: program to visualize data
• Optimization.py: the central program to run any model optimization
  • ALDONA_Classification.py: Tensorflow implementation of ALDONA algorithm
    • Neural_Attention_Model.py: Neural Network part of ALDONA
  • Baseline_Models.py: Tensorflow implementation of the baseline models
• Attention_Modules.py: Tensorflow implementation of position and context attention modules
• Algebra_Operators.py: matrix-tensor and tensor-tensor multiplication definitions
• Data_Neural.py: program to create left-context/aspect/right-context data
• Ontology_Reader.py: PYTHON3 implementation for the ontology reasoner

Directory explanation:

• Codes: folder containing all .py files
• Data: folder with all data files
  • Glove: directory for GloVe word embedding vectors ("glove.42B.300d.txt"). Two new files ("glove.42B.300d.txt.word2vec" and "glove.42B.300d_keys.txt") will be generated by MainData() in MAIN_Data_Generator.py
  • Numpy: directory for data (.npy format) which will be generated in the process
  • Test_data: directory for the input test file ("EN_REST_SB1_TEST.xml.gold"). It can be changed if xml structure remains the same.
  • Train_data: directory for the input train file ("ABSA16_Restaurants_Train_SB1_v2.xml"). It can be changed if xml structure remains the same.
• Ontology: directory for "ontology.owl"
• Results: directory for results stored in .csv format