TY - CONF TI - A Model of Integrating Bert and BiGRU+ Attention Dual-channel Mechanism for Investor Sentiment Analysis of Stock Price Forecast AU - Ma, Huawei AU - Ma, Jixin AU - Liang, Shengbin AU - Du, Wencai T2 - 2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) C1 - Taichung, Taiwan C3 - 2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) DA - 2022/12/07/ PY - 2022 DO - 10.1109/SNPD54884.2022.10051779 DP - DOI.org (Crossref) SP - 126 EP - 131 PB - IEEE SN - 9798350310412 UR - https://ieeexplore.ieee.org/document/10051779/ Y2 - 2023/04/26/07:33:11 ER - TY - CONF TI - A Model of Integrating Bert and BiGRU+ Attention Dual-channel Mechanism for Investor Sentiment Analysis of Stock Price Forecast AU - Ma, Huawei AU - Ma, Jixin AU - Liang, Shengbin AU - Du, Wencai T2 - 2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) AB - Investor sentiment and emotions have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors for stock price prediction using machine learning. Existing prediction models mostly depend on the analysis of trading data and company profit. few prediction theories have been built based on individual investors' sentiments. The fundamental reason is the difficulty to measure individual investors' sentiment. C3 - 2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) DA - 2022/12// PY - 2022 DO - 10.1109/SNPD54884.2022.10051779 DP - IEEE Xplore SP - 126 EP - 131 UR - https://ieeexplore.ieee.org/document/10051779 Y2 - 2024/01/14/15:26:28 ER - TY - CONF TI - A Machine Learning Approach to Predict the Trend of Obesity Prevalence at a Global Level AU - Barzinji, Ala Othman AU - Ma, Chaoying AU - Du, Wencai AU - Ma, Jixin T2 - 2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD) C1 - Zhuhai, China C3 - 2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD) DA - 2021/09/13/ PY - 2021 DO - 10.1109/BCD51206.2021.9581579 DP - DOI.org (Crossref) SP - 25 EP - 30 PB - IEEE SN - 9781728176819 UR - https://ieeexplore.ieee.org/document/9581579/ Y2 - 2023/04/26/07:37:11 ER - TY - CONF TI - A Comprehensive Review of Investor Sentiment Analysis in Stock Price Forecasting AU - Ma, Huawen AU - Ma, Jixin AU - Wang, Han AU - Li, Pengsheng AU - Du, Wencai T2 - 2021 IEEE/ACIS 20th International Fall Conference on Computer and Information Science (ICIS Fall) AB - Sentiment analysis technologies have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors. The objective of this paper is to evaluate the current state of the art and synthesize the published literature related to the financial sentiment analysis, especially in investor sentiment for prediction of stock price. Starting from this overview the paper provides answers to the questions about how and to what extent research on investor sentiment analysis and stock price trend forecasting in the financial markets has developed and which tools are used for these purposes remains largely unexplored. This paper represents the comprehensive literature-based study on the fields of the investors sentiment analytics and machine learning applied to analyzing the sentiment of investors and its influencing stock market and predicting stock price. C3 - 2021 IEEE/ACIS 20th International Fall Conference on Computer and Information Science (ICIS Fall) DA - 2021/10// PY - 2021 DO - 10.1109/ICISFall51598.2021.9627470 DP - IEEE Xplore SP - 264 EP - 268 KW - Analytical models KW - Information science KW - Investor sentiment analysis KW - Machine learning KW - Market research KW - Predictive models KW - Reliability KW - Sentiment analysis KW - Social media KW - Stock price trend forecasting KW - Tools ER - TY - JOUR TI - An Improved Double Channel Long Short-Term Memory Model for Medical Text Classification AU - Liang, Shengbin AU - Chen, Xinan AU - Ma, Jixin AU - Du, Wencai AU - Ma, Huawei T2 - Journal of Healthcare Engineering A2 - Li, Xingwang AB - There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model. DA - 2021/02/23/ PY - 2021 DO - 10.1155/2021/6664893 DP - USJ Library VL - 2021 SP - 1 EP - 8 J2 - Journal of Healthcare Engineering LA - en SN - 2040-2309, 2040-2295 UR - https://www.hindawi.com/journals/jhe/2021/6664893/ Y2 - 2022/04/28/11:43:23 ER -