Content Details | NUML Online Research Repository

Content Details

Back to Department Listing

Title Sentiment Analysis of Toxic Comment on Social Media using Deep Learning

Author(s) Huba Noor

Abstract In the rapidly evolving field of natural language processing, accurately predicting sentiments in text remains a critical challenge. This thesis addresses the problem by developing a novel multi-head model combining transformer-based architectures, DistilBERT and RoBERTa, with Bi-LSTM layers. Leveraging their complementary strengths, the model captures both global context and sequential dependencies in textual data. The research methodology involves extensive data preprocessing, model training, and evaluation using accuracy and F1-scores. Results demonstrate that the multi-head model outperforms traditional approaches, achieving a notable accuracy of 90.02%. This advancement offers significant benefits, including improved sentiment-driven decision-making and valuable insights across various industries, such as social media monitoring, customer feedback analysis, and market research.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date

Subject

Publisher

Contributor(s)

Format

Identifier

Source

Relation

Coverage

Rights