UPSI Digital Repository (UDRep)
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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| Sentiment analysis has become an indispensable tool in interpreting vast amounts of data generated from social media platforms, providing insights into public opinions on a variety of topics. This is particularly relevant in the context of emerging technologies like the Metaverse, which are frequently discussed across digital platforms. Traditional sentiment analysis tools struggle with the volume and complexity of data, particularly when dealing with nuanced discussions around new technologies such as the Metaverse. Convolutional Neural Networks (CNNs), known for their success in image processing, have not been extensively validated for their efficacy in analyzing sentiment from text-based social media data. This study introduces a novel approach by applying CNNs specifically to Metaverse-related tweets. This approach leverages CNNs’ ability to extract meaningful patterns from complex textual data, offering potentially superior performance over traditional models. We utilized a dataset of tweets explicitly gathered using keywords related to the Metaverse. After preprocessing which included text normalization and tokenization, a CNN model was trained to classify sentiments as positive or negative. The model’s architecture was optimized to handle the specific linguistic features of tweet data. The CNN model demonstrated high efficiency, achieving an accuracy of 96%, with precision and recall values exceeding 0.95. These results signify the model’s robustness in correctly classifying and understanding the sentiments expressed in tweets. The successful application of CNNs in this context suggests a powerful tool for businesses and researchers interested in quickly and accurately gauging public sentiment on emerging digital trends. This could lead to better-informed decision-making in marketing, product development, and policy-making. The findings encourage further exploration of CNNs in other NLP applications beyond traditional domains, reinforcing the versatility of deep learning in handling complex, real-world data challenges. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. |
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