# Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources
## 2 Related Work  
[PERSON] et al. [4] cited an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis. These results show, data extraction from SMN gives information in real-time and they measure sentiment analysis at a low cost of implementation. It assists to make smart decisions in several environments. This work pretty much similar to our work but they only focus on sentiment analysis whereas the RAIDSS model provides not only scraping and sentiment analysis but also gives chatbot informational decision, known and unknown sentence prediction, Topic data groupings, valid or invalid group data accuracy, and finally DMSS. [PERSON] et al. [6] discussed methods of multiclass text classification on unstructured data which is one of our approaches to doing topic labeling for data grouping. [PERSON] et al. [14] mentioned how to use Twitter data to send warnings and identify crucial needs and responses in disaster communication. [PERSON] et al. [15] described how to transform an openly available dataset into resources for urban planning and development. In text mining, [PERSON] et al. [16] proposed artificial intelligence for disaster response (AIDR), a platform to perform automatic text classification of crisis-related communications. AIDR classifies messages that people post during disasters into a set of user-defined categories of information. Above all, the whole process must ingest, process, and produce only credible information, in real-time or with low latency [17]. In our RAIDSS model, data can be extracted from various sources, and pre-processing gives the exact user intention via the visualization and informational chatbot application. Topic models have numerous applications in natural language processing. Numerous articles have been published on topic modeling approaches to different subjects, for example, social networks, software engineering, and linguistic sciences [18]. [PERSON] et al. [19] presented a review of topic models with delicate bunching capacities in text corpora, ex ploring essential ideas and existing models that sequenced different classifications with boundary estimations (i.e., Gibbs sampling) and performance evaluation measures. Likewise, [PERSON] et al. introduced a few uses of topic models for displaying text corpora and discussed a few open issues with future directions. In our case, topic modeling uses multi-class text classification that labels a significant corpus as a category. [PERSON] et al. (2020) reviewed the latest studies that employed deep learning (DL) to solve sentiment analysis problems, such as sentiment polarity. Models used term frequency-inverse document frequency (TF-IDF) and word embedding procedures on a series of datasets. Sentiment analysis comprises language preparation, text examination, and computational phonetics to recognize abstract sentiments ([PERSON] et al., 2020). For the most part, new data entry samples have a similar category ([PERSON] et al., 2020). Our model has an automated process for analyzing text data and sorting them into positive, negative, or neutral sentiments. The semantic text-mining approach is significant for text classification. [PERSON] et al. (2020) presented a practical semantic content-mining approach, which changes semantic data identified from a given set of documents into many novel highlights used for learning. Their proposed semantics-aware recurrent neural architecture (SRNA) empowers the system to obtain semantic vectors and raw text documents at the same time. This examination shows that the proposed approach beats a methodology without semantic information, with the highest exactness gained (up to 10% higher) from short reports. Our methodology also shows useful semantic content from a model of an application where unstructured data make up useful content. Most text classification and document categorization frameworks can be deconstructed into four stages: feature extraction, dimension reduction, classifier choice, and assessment.