# Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources
## 1 Introduction
In our system, we scraped Twitter SMN data where several data fields construct our model and visualize data in a user's intended way. Keeping up information quality is a troublesome yet fundamental undertaking. To accomplish predictable and dependable information, the model should continually oversee information quality so they build authenticity and enable quicker which produce more proficient decisions. We applied semantic, syntactic, consistency, completeness, and uniqueness to maintaining data quality. After removing repetition and contradictory data, it decreases the original length of size and provides freshness, timeliness, and actuality. The measurement is the interaction that actualizes the metric to acquire the value on the dimension factor. In a similar example of our dataset which is Covid data, the dimension exactness, the accuracy factor, the distinction in data field metric can be assessed by an evaluator utilizing a data cleaning function. For dimensionality reduction and increasing model accuracy, our total dataset reducing 3590 rows to 1795 rows with seven topic labeling. We have applied the same methodology in the Disaster dataset for our model verification. The scraping classifier in Section 4.1 has a Covid data field dimension which is based on the Twitter user's statement. In supervised learning, there is a point at which we need to initially prepare the model with a previously existing, named dataset, much the same as showing a child how to differentiate between a seat and a table. We need to uncover disparities and similitudes. In contrast, unsupervised learning is tied to learning and predicting without a pre-named dataset. In the proposed RAIDSS model, there are two kinds of information input strategies: one is using labeled data, and the other is data mining. Users can input both types of information. Therefore, text classification is one of the most significant cycles for characterizing the user's given info, choosing to order the data for unsupervised or supervised learning. If the data contain labeled information, then the text classifier and pre-processor execute data-wrangling extraction. After finishing a model assessment, the application takes the data for service and prediction. In contrast, if the information originates from Web scraping of various sources, numerous things (like data mining and analysis) need to be handled. The classifier's objective should be to record clean information from the user and return the desired output. In discovering clean information segments, we need to conduct sentiment analysis to measure information execution and visualization. Topic modeling through Latent Dirichlet Allocation (LDA) and raw data conversion turn this unsupervised learning into a labeled dataset after information assembling, which provides structural performance and results. We propose a real-time AI-based informational decision support system (RAIDSS) model for informational and DMSS systems in text classifications that include the following terminologies:  
* A filter cleaning text (FCT) methodology to scrape data cleaning and groupings;
* A word generative probabilistic (WGP) method for highest word-frequency label selection;
* A context-based chatbot application based on scraped datasets. For the decision-making support system, the RAIDSS model visualizes data mining for the analysis of the topics (e.g., the current novel Covid and Disaster corpus); using Twitter data (namely tweets) for sentiment analysis; applying topic labeling for unsupervised and supervised learning (multi-class text classification); hyper-tuning data to provide robust application efficiency; visualizing data in various graphs, and comparing text classification methods. Finally, the chatbot provides an informational decision from among the supervised and unsupervised processes. The remainder of this paper is organized as follows: Section 2 presents related work from the literature. Section 3 outlines the methodology of the working procedure in a system model.