Sentiment Analysis of Social Media Text Based on Deep Learning

This study delves into the application of deep learning in sentiment analysis of social media text, employing Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. Our experimental results showcase the remarkable potential of these models in effectively capturing sentiment information, achieving high accuracy, and maintaining a balanced trade-off between precision and recall. The study underscores the crucial role of feature representation in sentiment analysis, emphasizing the benefits derived from employing word embedding techniques to enhance model performance. Through careful hyperparameter tuning, involving learning rates, batch sizes, and hidden layer sizes, we optimize model performance, further increasing sentiment analysis accuracy. The findings highlight the transformative potential of deep learning in unraveling sentiment nuances embedded in social media text, offering profound insights for decision support in society. Me anticipate that multimodal sentiment analysis and cross-cultural sentiment analysis will emerge as significant research directions in the field of deep learning sentiment analysis. The study not only contributes to the understanding of sentiment analysis but also points towards a future where deep learning plays a pivotal role in providing a deeper emotional understanding of social media content, thus enriching the landscape of decision support in a societal context.