A NOVEL HYBIRD CNN AND LEXICON-BASED MODEL FOR DETECTING PUBLIC SENTIMENT OF DANANTARA
Abstract
Danantara is a sovereign wealth fund that has received widespread attention on social media, including on "X." Various public opinions about Danantara have emerged, both positive and negative. To understand public perception more accurately, an effective sentiment analysis method is needed. Lexicon-Based methods are often used but have limitations in capturing context, while Convolutional Neural Networks (CNNs) are able to recognize patterns in text but require a lot of data. Therefore, this study aims to develop a hybrid CNN and Lexicon-based model to improve the accuracy of public sentiment analysis towards Danantara. Data will be collected from social media "X" using scraping or API methods, then go through a preprocessing process before being analyzed using a hybrid approach. Model evaluation is carried out by comparing the performance of CNN, Lexicon-based, and hybrid models using metrics such as accuracy, precision, recall, and F1-Score. The results of this study provide a more accurate model in understanding public opinion and provide insights for policymakers in designing more effective communication strategies with an accuracy rate of 86%. In addition, researchers will also contribute to the development of Natural Language Processing (NLP) and deep learning-based sentiment analysis.
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