Sentiment Analysis in Social Media Using Deep Learning Techniques

This work offers a thorough investigation of sentiment analysis in social media using deep learning techniques. Understanding stoner sentiment has become essential for colourful operations like marketing, public opinion research, and recommendation algorithms due to the rapid expansion of social media networks. Deep learning approaches have emerged as crucial tools for sentiment analysis tasks because of their capacity to extract intricate patterns from massive datasets. The usefulness of deep learning models, such as convolutional neural networks (RNNs), and intermittent neural networks (RNNs), and their variations, in extracting complex sentiment information from social media textbooks is investigated in this study. Additionally, the study looks at model infrastructures, point representations, and pre-processing techniques to improve sentiment analysis performance. The results show how effective deep learning techniques are in extracting sentiment directly from various social media posts, opening the door for more sophisticated sentiment-apprehensive digital processes