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Sentiment Analysis of Sinhala News Comments

Summary

This project contains all submodules for building a sentiment classification workflow for Sinhala news comments. Project contains both Java and Python source code, where, data acquisition process is developed in Java and data analysis and classification processes are developed in python. Project also contains the dataset used for the analysis.

Sentiment classification process involves following steps.

  1. Crawling and collecting data
  2. Initial data filtering and preparing for data labeling process
  3. Labeling data (comments) on its sentiment value
  4. Further pre-processing data and preparing for classification
  5. Classification and result interpretation

Details

Downloading Word2Vec Models

Since Word2Vec models are larger than 100MB, they can't be stored in git as ordinary files. Previously, they were stored using git-lfs. But due to free limits, now you can find these models including code to generate them in this Google Drive directory. Copy models into $PROJECT/corpus/analyzed/saved_models/ directory.

Directory Structure and Files

+-- corpus - data files and generated models  
|   +-- analyzed  
|   |   +-- saved_models  
|   +-- raw_data  
|   +-- tagged  
+-- src/main/java - Java source code (data collection, tagging and cleaning)  
+-- src/main/python - Python source code (pre-processing, analysis/classification)  
+-- src/main/resources - static resource (html, properties)  
+-- README.md    
+-- pom.xml

Dataset

Summary of dataset

corpus directory contains dataset and analysed models. raw_data: This directory contains news articles with associating comments. These files contains original news articles before any of the preprocessing steps. analyzed: This directory contains the pre-processed file and generated models saved_models: Holds generated models (eg. word2wec model)

Word2Vec Models

Generated Word2Vec models can be found under /corpus/analyzed/saved_models. These models were generated using only the comments of the articles (article content was not used for generating models).

Results

+---------------------+----------------+----------------+----------------+----------------+
|      Algorithm      |    Accuracy    |   Precision    |     Recall     |    F1_Score    |
+---------------------+----------------+----------------+----------------+----------------+
|     Naive Bayes     | 0.776946107784 | 0.840740740741 | 0.690690690691 | 0.752902155887 |
|    Random Forest    | 0.859281437126 | 0.91159586682  | 0.794794794795 | 0.849197860963 |
| Logistic Regression | 0.867764471058 | 0.916099773243	| 0.808808808809 | 0.859117490696 |
|         SVM         | 0.869261477046 | 0.923076923077 | 0.804804804805 | 0.859893048128 |
|      RNN LSTM       | 0.864583331347 | 0.891712707182	| 0.853146853147 | 0.861719167111 |
+---------------------+----------------+----------------+----------------+----------------+

Note

Dataset used in this project is collected by crawling Sinhala online news sites, mainly www.lankadeepa.lk.

contact

Please contact us if you need more information.

cite

If you use this data, code, or the trained models, please cite this work
Ranathunga, S., & Liyanage, I. U. (2021). Sentiment Analysis of Sinhala News Comments. Transactions on Asian and Low-Resource Language Information Processing, 20(4), 1-23.