Sentiment Study of ChatGPT on Twitter Data with Hybrid K-Means and LSTM
Analisis Sentimen Berdasarkan Hasil Klasterisasi K-Means pada Data Pengguna ChatGPT Menggunakan LSTM
DOI:
https://doi.org/10.30812/matrik.v24i2.4791Keywords:
Chat-Gpt, K-Means, Long Short-Term Memory, Sentiment Analysis, TF-IDF, Word2VecAbstract
The rapid evolution of artificial intelligence (AI) has transformed the way people interact with technology, with ChatGPT emerging as a standout innovation in natural language processing (NLP). While it offers immense benefits, such as improving productivity and accessibility, it has also sparked debates about trust, transparency, and user experience. This makes understanding public sentiment about ChatGPT both timely and essential.This study explores user sentiments by combining K-Means clustering and Long Short-Term Memory (LSTM) models for analysis. The research utilized a dataset from Kaggle, which underwent extensive preprocessing, including text cleaning, tokenization, and lemmatization. Key features were extracted using TF-IDF and Word2Vec techniques, while clustering was refined with the Elbow Method and Silhouette Score. The data was grouped into three clusters focusing on ChatGPT’s functions, its developers, and user activities. Sentiment analysis using LSTM achieved an impressive accuracy of 98% after five training cycles. The findings highlight that negative sentiments, particularly around technical challenges and transparency, dominate user feedback, signaling areas for improvement. While positive sentiments exist, they remain overshadowed by critical perspectives. This study underscores the importance of enhancing user trust and experience while ensuring ethical and transparent AI development. The insights provided aim to guide developers and policymakers in creating AI technologies that are more user-focused and socially responsible. Future research should include multilingual and cross-platform data to paint a more comprehensive picture.
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