Semiconductor Industry Trend Prediction with Event Intervention Based on LSTM Model in Sentiment-Enhanced Time Series Data
Wei-hsiang Yen, Lyn Chao-ling Chen

TL;DR
This paper presents an LSTM-based model integrating sentiment analysis of textual data and event interventions to accurately predict semiconductor industry trends, specifically focusing on TSMC's market developments and technological advancements.
Contribution
It introduces a novel approach combining sentiment analysis with event intervention in time series data for industry trend forecasting, enhancing prediction accuracy over traditional methods.
Findings
Successfully predicted TSMC's wafer technology development
Identified potential threats in the global market
Aligned predictions with TSMC's product releases and international news
Abstract
The innovation of the study is that the deep learning method and sentiment analysis are integrated in traditional business model analysis and forecasting, and the research subject is TSMC for industry trend prediction of semiconductor industry in Taiwan. For the rapid market changes and development of wafer technologies of semiconductor industry, traditional data analysis methods not perform well in the high variety and time series data. Textual data and time series data were collected from seasonal reports of TSMC including financial information. Textual data through sentiment analysis by considering the event intervention both from internal events of the company and the external global events. Using the sentiment-enhanced time series data, the LSTM model was adopted for predicting industry trend of TSMC. The prediction results reveal significant development of wafer technology of TSMC…
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Taxonomy
TopicsForecasting Techniques and Applications · Stock Market Forecasting Methods · Industrial Vision Systems and Defect Detection
