Leveraging Natural Language Processing and Deep Learning for Sentiment Analysis on social media Big Data

Abstract

Social media generates vast amounts of textual data on a daily basis. Performing sentiment analysis on this data can provide valuable insights into public opinion and trends. However, the size and unstructured nature of social media data present challenges for traditional sentiment analysis techniques. This paper explores how natural language processing and deep learning can be leveraged to perform more nuanced and scalable sentiment analysis on social media big data. We provide an overview of key natural language processing techniques like part-of-speech tagging, named entity recognition, and word embeddings. We then examine popular deep learning architectures like convolutional neural networks and long short-term memory networks that have achieved state-of-the-art results on sentiment analysis tasks. Pretrained language models like BERT are also discussed as a means of enhancing deep learning models with semantic knowledge. We present two case studies demonstrating how these technologies can be combined and customized for sentiment analysis on distinct social media datasets from Twitter and Reddit. Our results indicate that deep learning approaches utilizing bidirectional encoder representations from transformers (BERT) and convolutional neural networks consistently outperform traditional machine learning algorithms like support vector machines. The insights provided by sentiment analysis on social media data are valuable for fields ranging from marketing to sociology. This paper shows how recent advances in natural language processing and deep learning can enable more sophisticated sentiment analysis on large-scale, noisy social media data.