Year: 2024
Type: dissertation
Abstract: In the realm of Natural Language Processing (NLP), sentiment classification stands as an essential subject, commonly categorizing text into three classes: positive, negative, and neutral, enabling the statistical analysis of the emotional tendencies in the text.With the development of social networks, the classification requirements for sentiment analysis have gradually become more refined.This thesis draws datasets related to hate speech, fine-tuning BERT as a classification model to categorize the text into five categories to enhance the analysis of hate speech within social media platforms.Simultaneously, to address the substantial parameter size of large language models and the associated high fine-tuning costs, the thesis explores three methods for fine-tuning the BERT model: Fine-tuning BERT with all parameters, Fine-tuning BERT only the classification heads and Fine-tuning BERT with LoRA.This Thesis empirically validates the feasibility and superiority of designing targeted sentiment analysis models for refined scenarios.Achieving 90% accuracy confirms the outstanding performance of the BERT model in addressing hate speech analysis issues.Furthermore, comparing different fine-tuning methods validates the potential and value of the PEFT method for fine-tuning transformer models.
Author: Zhao, Yingqi
Institution: Tampere University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7133093837