Harnessing Convolutional Neural Networks for Sentiment Analysis of Tweets on the Metaverse

In the digital era, understanding public sentiment towards emerging technologies such as the Metaverse is crucial for businesses and developers. Traditional sentiment analysis methods often struggle to accurately interpret the nuances of digital communication, particularly when assessing rapid technological advancements. This study harnesses the power of Convolutional Neural Networks (CNNs) to address this challenge, focusing on sentiment analysis of tweets related to the Metaverse. By employing advanced preprocessing techniques including tokenization and vectorization, and by using a CNN model, we have analyzed a dataset of tweets to discern public opinion with high precision. The CNN demonstrated remarkable effectiveness, achieving an overall accuracy of ${9 7 \%}$ with precision, recall, and F1-scores exceeding 0.96 for both positive and negative sentiments. These results underline the potential of CNNs in extracting meaningful insights from social media data, which can be pivotal for shaping marketing strategies and understanding consumer behavior towards new technologies like the Metaverse. The findings suggest that leveraging such deep learning models could significantly enhance the accuracy and depth of sentiment analysis in digital communication landscapes.