Sentiment analysis of Sinhala language using deep learning networks

1. Description
The main objective of the project is to test deep learning models to identify the sentiments in Sinhala text. A Facebook dataset is used to train and test the models. The model codes are already developed and only the training and testing phases remain to be done. Since Sinhala remains as a resource poor NLP language, this project will lend a hand to improve the current tools and provide insight on the current state.
Migrating the current code into JAX with the use of Flax, Haiku and other libraries is another objective. Libraries like Trax with basic deep learning models and the trending Transformers are aimed to be tested.

2. Language
The models are trained in Sinhala Language

3. Model
The models that will be tested are

RNN
LSTM
GRU
BiLSTM
Baseline  models  with  the  combination  of  a CNN
Stacked LSTM and BiLSTM
HAHNN
Capsule networks


4. Dataset
A Facebook dataset contaning 526,732 Sinhala and English posts extracted from CrowdTangle . The dataset consists of a decade’s worth of content from Facebook pages popular in Sri Lanka.

5. Training scripts
The following links contain the model scripts
Main models 1

6. Challenges
There are several models that needs to be adjusted and tested

7. Desired project outcome
Performance measures of each model

8. Reads
The following links can be useful to better understand the project and
what has been done previously.
https://sencat.lk/ 1