# Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources
## 1 Introduction  
Information mining is a cycle that finds relevant patterns from a large amount of data. After collecting these data, text classification (which depends on the content and which dynamically classifies many texts from different fields on the internet) builds an innovative system, relationship, and decision through natural language processing (NLP) [1]. Clustering, classification, information extraction, and information mining include various text preparation steps needing powerful data models because of inconsistencies and non-standard noise in digitized messages. In NLP, text arrangement is considered difficult because of the different types of information representation [2]. Social media data mining is used to uncover hidden patterns and trends from social media network (SMN) platforms like Twitter, LinkedIn, Facebook, and others [3]. There is unstructured content on social media--like tweets, comments, status updates--which is not only based on businesses, firms, and agencies but also have Public Protection Disaster Relief (PPDR) and DMSS-related information [3]. [PERSON] et al. [4] mention anonymous real-time data to generate information which allows sentiment analysis on a given subject and [PERSON] et al. [5] cite classification for sentiment analysis. [PERSON] et al. [6] analyzed multiclass text classification on noisy data. To improve the performance of DSS, [PERSON] et al. [7] used intelligent techniques for traffic prediction and [PERSON] and [PERSON] [8] applied intelligent agents for transportation management. Learning methods includes supervised and unsupervised methods, [PERSON] [9] for rule extraction, [PERSON] et al. [10] for traffic risk analysis, and [PERSON] et al. [11] for traffic prediction. [PERSON] et al. [12] also investigated the effects of learning methods in DSS based on historical data. [PERSON] et al. [13] quote supervised and unsupervised learning decision support systems for incident management in an intelligent tunnel. Most of the research work examines only particular disasters or specific event analysis based on existing or scraping datasets, but comparative research on a decision support system that gives reliable information has not been extensively applied during a large or diverse set of crises. Aiming to provide real-time information for DMSS, this research provides data collecting and proposed methods for data cleaning and grouping, sentiment analysis, topic labeling for data categorization, chatbot application for sentence-based decision-making, unknown sentence prediction and decision, and finally DMSS from data visualization. Our novelty of this work is divergent from others because the model arranges user's statements regarding the real-time situational information from any large or short corpus which encourages them to make an event analysis, visualization, informational data support by a chatbot, and DMSS. In addition, we experimented with short corpus data which gives informed decisions based on its authenticity. If the model gives an informational decision within a short dataset, it can provide also good accuracy and decisions among the larger ones. For this reason, we did evaluations with both short corpus (unsupervised) and larger corpus (supervised) chatbot in Section 6 and shows their sentence accuracy label which is both cases (>98%). We achieved 96% and 99% accuracy in test and training datasets by using LSTM on a short corpus in the DL method. Moreover, the ML method using hyper-parameter in the dataset, a random forest algorithm increases data accuracy in the F1 score which is 2% (81-83%). We used a completely different dataset for model verification, which is the Disaster corpus. The previous corpus contained 1635 sentences, whereas this corpus contains 10,875 sentences. Surprisingly, the data behavior that is employed in decision-making appears to be comparable in both circumstances. Furthermore, the deep learning LSTM model has an accuracy of over 98% in both Disaster and Covid scenarios.