Abstract:
In today's digital age, social media platforms have emerged as powerful means of
expressing opinions, sharing experiences, and shaping public discourse. The abundance of
user-generated content on platforms such as Twitter, Facebook, and Reddit provides
information for studying public sentiment and opinion dynamics. This sentiment analysis
project uses advanced natural language processing (NLP) techniques to analyze sentiment
in English social media data, giving users valuable insights into the prevailing sentiments
on various topics.
The study employs advanced natural language processing techniques, such as machine
learning algorithms and deep learning architectures. The proposed enhancements include
creating a robust sentiment lexicon designed to capture the diversity found in social media
content. Furthermore, the study investigates the success of transfer learning approaches in
adapting sentiment analysis models, reducing the need for language-specific labeled
datasets.
Furthermore, the study considers the dynamic nature of social media content. A real-time
sentiment analysis framework is proposed, incorporating continuous learning mechanisms
to adapt the model to emerging patterns on social media platforms.
The proposed enhancements are evaluated through comprehensive experiments across
diverse datasets. Performance metrics, including precision, recall, and F1 score, are
employed to quantify the proposed methods' effectiveness in comparison to existing
approaches.
The findings of this research contribute to the advancement of sentiment analysis
methodologies in the context of English social media data, offering practical insights for
applications such as brand monitoring, public opinion analysis, and social media trend
prediction on a global scale. The proposed enhancements aim to foster more accurate and
culturally aware sentiment analysis models, facilitating a deeper understanding of nuanced
expressions within social media communication's vast and diverse landscape.
