| dc.description.abstract | With the development of technology, investors can obtain the latest market intelligence of financial products through multiple channels, and it also increase the time cost of analyzing information. It is difficult to accurately determine the appropriate investment timing through independent analysis. Therefore, how to master different aspects of stocks Information on buying and selling decisions has become a significant key to profit.
This research mainly proposes the use of static and dynamic word embedding for the content of the text, and the establishment of sentiment analysis models through machine learning and deep learning classifiers to predict the sentiment scores of articles in social media, and then combine structured data to make stock predictions. In the past research on sentiment analysis of texts, although many researches used word embedding methods to establish sentiment analysis models for stock-related texts to distinguish their sentimental meanings to predict the future rise and fall trends of stocks, but they didn’t use this method compare with other sentiment methods to judge the applicability of different sentiment analysis methods in stock forecasting. Therefore, this research will construct word representation sentiment analysis
model is compared with traditional dictionary methods and Vader API to find sentiment analysis methods that are conducive to stock forecasting.
This study collected three social media text data (Twitter, Instagram, Facebook), combined with different sentiment analysis methods (dictionary, API, static word embedding sentiment analysis model), and four classifiers, Random Forest , Naïve Bayes, LSTM, PF-LSTM to classify, expect to understand the effect of different sentiment analysis methods on stock prediction. In the end, this experiment found Bidirectional Encoder Representations from Transformers and Sentiment Orientation emotional analysis model can identify the emotional meaning in the social media and has a positive impact on the stock price prediction. The accuracy rate is as high as 70.15%, which is the best sentiment analysis model. | en_US |