Improving the Accuracy of Public Sentiment Analysis on the Presence of Artificial Intelligence using an NLP-based Hybrid Deep Learning Model

Daniel Sintong Pardamean Simanjuntak, FA Bambang Sukoco, Ahmad Husaein, Muhammad Zidan

Abstract


The development of Artificial Intelligence (AI) has generated diverse public responses, which are widely expressed through social media platforms such as Twitter/X, TikTok, and YouTube. Understanding public sentiment toward AI is important because it provides insights into public acceptance, concerns, and perceptions regarding the development of this technology. However, sentiment analysis of Indonesian-language social media text remains challenging due to the informal and unstructured nature of the language, including slang, typographical errors, and substantial morphological variations. This study aims to develop a hybrid sentiment analysis model based on Bidirectional Long Short-Term Memory–Conditional Random Fields (BiLSTM–CRF), integrating Word2Vec and FastText to improve the performance of public sentiment classification toward Artificial Intelligence. The dataset was collected from three social media platforms—Twitter/X, TikTok, and YouTube—and comprised 30,550 comments after preprocessing and labeling into three sentiment categories: positive, neutral, and negative. The experiments compared the proposed models with the baseline CRF, LSTM, and BiLSTM models using accuracy, precision, recall, and F1-score as evaluation metrics. The results show that the BiLSTM–CRF model with FastText achieved the best performance, with an accuracy of 93.12%, precision of 92.74%, recall of 92.31%, and F1-score of 92.52%. This performance exceeded that of BiLSTM–CRF with Word2Vec (91.38%), BiLSTM (87.24%), LSTM (84.15%), and CRF (78.42%). These findings demonstrate that integrating BiLSTM–CRF with FastText's subword-based representation is effective in improving sentiment analysis performance on Indonesian-language social media data. This study contributes to the development of more robust, adaptive, and representative sentiment analysis methods for understanding public opinion toward Artificial Intelligence.

Keywords


artificial intelligence; BiLSTM–CRF; fasttext; social media; sentiment analysis

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DOI: https://doi.org/10.32520/stmsi.v15i9.6737

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