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Nepali Offensive Language Detection and Sentiment Analysis

This repository contains the code and datasets for the Nepali Offensive Language Detection and Sentiment Analysis project.

Abstract

Addressing the issue of offensive content in the digital realm has become increasingly vital due to the widespread use of online platforms and social media. Nevertheless, the intricacies arising from linguistic diversity present challenges that diminish the efficacy of pre-trained models originally tailored for English or other specific languages in multilingual contexts. To tackle this issue, our research endeavors to thoroughly investigate the limitations and obstacles associated with applying pre-trained models to tasks related to detecting offensive language in multilingual settings.

This study specifically concentrates on the development and assessment of a system crafted for the identification of offensive entities in Nepali. The objective is to enhance content moderation tools and promote safer online environments for the Nepali-speaking community. The research extensively explores the effectiveness of various pre-trained BERT architectures in Named Entity Recognition (NER), with a particular focus on aspect term extraction to precisely categorize abusive entities in Nepali text. Furthermore, the scope of the study extends to Sentiment Analysis (SA), with a concentration on sentiment classification in the Nepali language. Through this dual approach, our research aims to bolster content moderation tools and establish safer online spaces for the Nepali-speaking community. Ultimately, our overarching goal is to pave the way for improved cross-linguistic understanding and enhanced model performance across a diverse array of languages.

Objectives

  • Develop and evaluate a system tailored for identifying offensive entities in Nepali, contributing to the improvement of content moderation tools and the creation of safer online spaces for the Nepali-speaking community.

  • Investigate the effectiveness of various pre-trained BERT architectures, with a specific focus on Named Entity Recognition (NER) for aspect term extraction. The goal is to accurately categorize abusive entities in Nepali text.

  • Extend the research scope to include Sentiment Analysis (SA), concentrating on sentiment classification in the Nepali language. This dual approach aims to enhance content moderation tools and establish safer online spaces for the Nepali-speaking community.

Key Features

  • In-depth exploration of linguistic challenges in offensive language detection for a multilingual context.
  • Evaluation of pre-trained BERT architectures for Nepali, with a particular emphasis on Aspect term extraction using NER and comprehensive analysis of sentiment classification in detecting Offensive Nepali language.

Folder Structure

  • NepSA: Code and resources for the Sentiment Analysis task.

    • datasets: Folder containing datasets related to Sentiment Analysis.
    • NepSA.ipynb: Jupyter notebook for running the Sentiment Analysis code.

  • NepNER: Code and resources for the Offensive Language Detection using Named Entity Recognition (NER) task.

    • datasets: Folder containing datasets related to NER.
    • NepNER.ipynb: Jupyter notebook for running the NER code.

  • Paper_and_Slides: Contains the paper and powerpoint slides for the project.

  • nepsa-nepner-inference: Contains code to inference on saved models.

Dependencies

  • Python 3.x
  • Jupyter Notebook
  • Libraries: PyTorch, Transformers, scikit-learn, pandas, numpy, matplotlib, seaborn, and any other dependencies mentioned in the notebooks.

Running on Local Machine

  1. Clone the Repository:
  • Open a terminal.
  • Run the following command to clone the repository:
git clone https://github.com/merishnaSuwal/nep-off-langdetect.git
  1. Navigate to Folder and install the dependencies:
cd nep-off-langdetect
pip install -r requirements.txt
  1. Run Experiments: Explore the Jupyter notebooks and scripts in the repository to run experiments, evaluate models, and analyze results.

Running the Code on Google Colab

  1. Import the notebook in Google Colab.
  2. Ensure that the required dependencies are installed and execute the cells one by one.

Results and more information

Our research strives to contribute valuable insights into the effectiveness of pre-trained models for offensive language detection in Nepali. The findings are detailed in the corresponding research paper, highlighting advancements in aspect term extraction and sentiment analysis for the Nepali language.

You can also view the demo presentation of this project on YouTube.

Future Work

The project opens avenues for future research focusing on:

  • Continued refinement of offensive language detection models for Nepali.
  • Exploration of additional linguistic nuances and dialects within the Nepali language.
  • Collaboration with the community to address specific challenges and further improve model performance.

Contributors

  • Merishna Singh Suwal
  • Sujan Bhusal
  • Drishtant Regmi

Feel free to contribute to this project by forking the repository and submitting pull requests.