Natural Language Processing-Based Financial Time Series Forecasting: Utilizing Sentiment Analysis for Improved Stock Price Prediction
This study explores the application of natural language processing (NLP) techniques in financial time series forecasting, specifically in predicting stock prices. Historical stock price data and textual data from financial news articles and social media sources were collected, and TextBlob was used...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Pusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri Cilacap
2025-06-01
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| Series: | Journal of Innovation Information Technology and Application |
| Subjects: | |
| Online Access: | https://ejournal.pnc.ac.id/index.php/jinita/article/view/2290 |
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| Summary: | This study explores the application of natural language processing (NLP) techniques in financial time series forecasting, specifically in predicting stock prices. Historical stock price data and textual data from financial news articles and social media sources were collected, and TextBlob was used to obtain sentiment indices from the textual data. A hybrid model combining NLP techniques with LSTM (Long Short-Term Memory) neural networks was developed, and the methodology involved preprocessing and analyzing textual data using sentiment analysis with TextBlob and integrating the sentiment indices with historical stock price data for forecasting with LSTM. The LSTM model achieved a performance of 89.6 percent precision and outperformed traditional time series forecasting models in terms of accuracy and reliability. The results demonstrate that incorporating sentiment indices obtained through NLP significantly enhances the predictive performance of stock price forecasting models, and the study highlights the potential of NLP techniques, particularly sentiment analysis with TextBlob, in conjunction with LSTM neural networks, to improve the accuracy of financial time series forecasting, specifically in predicting stock prices.
Studi ini mengeksplorasi penerapan teknik pemrosesan bahasa alami (Natural Language Processing/NLP) dalam peramalan deret waktu keuangan, khususnya untuk memprediksi harga saham. Data harga saham historis dan data tekstual dari artikel berita keuangan serta sumber media sosial dikumpulkan, dan TextBlob digunakan untuk memperoleh indeks sentimen dari data tekstual tersebut. Sebuah model hibrida yang menggabungkan teknik NLP dengan jaringan saraf LSTM (Long Short-Term Memory) dikembangkan, dan metodologinya melibatkan praproses dan analisis data tekstual menggunakan analisis sentimen dengan TextBlob, serta integrasi indeks sentimen dengan data harga saham historis untuk peramalan menggunakan LSTM. Model LSTM ini mencapai kinerja dengan tingkat ketepatan (precision) sebesar 89,6 persen dan mengungguli model peramalan deret waktu tradisional dalam hal akurasi dan keandalan. Hasilnya menunjukkan bahwa penggabungan indeks sentimen yang diperoleh melalui NLP secara signifikan meningkatkan kinerja prediktif model peramalan harga saham, dan studi ini menekankan potensi teknik NLP, khususnya analisis sentimen dengan TextBlob, dalam kombinasi dengan jaringan saraf LSTM, untuk meningkatkan akurasi peramalan deret waktu keuangan, khususnya dalam memprediksi harga saham. |
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| ISSN: | 2716-0858 2715-9248 |