A Novel Technique for Analyzing the Sentiment of Social Media Posts Using Deep Learning Techniques

Our study aims to precisely categories the sentiment expressed in user-generated text, concentrating specifically on Twitter data. Using a benchmark dataset of labelled tweets, we evaluate the efficacy of our proposed method to that of traditional machine learning approaches, such as Support Vector Machines (SVM) and Naive Bayes (NB). Our methodology entails preprocessing the text data by tokenizing, removing stop words, and stemming, followed by feature extraction using word embedding’s. For sentiment classification, we employ a Convolutional Neural Network (CNN) architecture with multiple convolutional layers and pooling operations. In terms of accuracy, precision, recall, and F1 score, the experimental results indicate that our proposed deep learning method outperforms conventional machine learning techniques. In addition to this, we do an error analysis in order to identify challenging scenarios and give insight into the constraints as well as prospective improvement areas. The results of this research provide a significant contribution to the field of social media sentiment analysis and provide evidence of the usefulness of deep learning algorithms for the correct categorization of sentiments in Twitter data.