Enhanced Text Classification Using DistilBERT with Low-Rank Adaptation: a Comparative Study

EasyChair Preprint no. 13820

Abstract

In this article, we delve into the task of sentiment analysis applied to news articles covering sanctions against Russia, with a specific focus on secondary sanctions. With geopolitical tensions influencing global affairs, understanding the sentiment conveyed in news about sanctions is crucial for policymakers, analysts, and the public alike. We explore the challenges and nuances of sentiment analysis in this context, considering the linguistic complexities, geopolitical dynamics, and data biases inherent in news reporting. Leveraging natural language processing techniques and machine learning models, including Large Language Models (LLM), Convolutional Neural Networks (Conv1D), and Feed-Forward Networks (FFN), we aim to extract sentiment insights from news articles. Our analysis provides valuable perspectives on public opinion, market reactions, and geopolitical trends. Through our work, we seek to illuminate the sentiment landscape surrounding sanctions against Russia and their broader implications.

Keyphrases: Geopolitical Tensions, sanctions, Secondary Sanctions, Sentiment Analysis

@Booklet{EasyChair:13820,
  author = {Brice Donald Abodo Eloundou and Wang Quanyu},
  title = {Enhanced Text Classification Using DistilBERT with Low-Rank Adaptation: a Comparative Study},
  howpublished = {EasyChair Preprint no. 13820},

  year = {EasyChair, 2024}}