# An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements
## 2 Related Work
### Defining the Academic Gaps and Research Questions  
Based on the aforementioned analysis, it is concluded that there are various methods in the literature that deal with the stock prediction. These methods utilize either statistical methods or AI methods including DNNs. Most of the presented methods use historical values of the stock prices in order to predict the forthcoming prices, while there are others that try to predict stock prices through sensing the general sentiment on social media.  
Even though there are many works dealing with stock prediction, there are still some gaps that have to be considered. First, for the stock prediction, most of the existing works are based on stocks' historical values. This approach lacks of considering other information that may be significant, such as social or political events that are depicted in news articles and social media posts. Such information may lead to the early detection of critical changes in the trends of stock prices. [PERSON] and [PERSON] [7] proved that such information can be considered by sensing public sentiment on social media. Thus, the monitoring and analysis of social medias' sentiment can be of added value for the stock prediction. In this aspect, there is a lack of platforms that handle and combine both historical stock prices and the general sentiment for the investment recommendation. Second, although there are many platforms or systems that provide advice for investments, also called robo-advisors, they usually act automatically without providing to the investors the information that led to their recommendations. Thus, the interpretability of their recommendations is not well established. Finally, there is the need for an interactive platform that will collect and analyze data in a daily basis and provide investment recommendations not only for specific assets but for an investor's portfolio. The suggestions should be personalized to the profile of the users so that different advice can be provided to risky and conservative investors.  
Following the above research gaps, we propose ASPENDYS, an interactive web-platform that offers supportive information to investors, thus facilitating the decision making related with stock investments. The platform consists of several tools and methods that utilize key technologies of the computer science. These include Natural Language Processing (NLP), a sub-field of computer science, and artificial intelligence that can accurately extract information and insights contained in the textual data as well as categorize and organize these data. Additionally, in the ASPENDYS platform, deep learning (DL) technologies were used, which is a sub-field of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.  
The research questions (RQ) that have arisen are as follows:RQ1: Is the ASPENDYS platform able to provide investment recommendations by combining both historical stock prices and the extracted sentiment, for an asset, through the sensing of social media and news websites?  
RQ2: Can ASPENDYS platform be used as a supportive tool for investors that will assist their investment decision making and monitoring?