The development of Big Data (BD), which is used to obtain numerous data from various domains, is brought about by technological advancement. However, managing the information and extracting knowledge from it is the most challenging and problematic. Thus, this paper proposed a template-centric new Data Acquisition (DAQ) methodology. The stock market data is gathered from several structured or unstructured data sources. After the DAQ criterion, templates are created for the gathered data. The stock market data is collected grounded on its Application Programming Interface (API) and transmitted via the transmission protocols during the DAQ process. To effectively remove redundant data, the transmitted data is pre-processed and stored efficiently in the network for further real-time analysis. Finally, the proposed technique’s performance is evaluated. As per the experimental and empirical evaluation, the proposed system surpasses the other methods.

The prediction of stock market prices based on the financial text sentiment classification using Machine Learning (ML) and Deep Learning (DL) models is becoming popular among researchers in the era of Big Data (BD). Nevertheless, owing to the lack of extensive analysis, most of the developed ML and DL models failed to achieve better classification results. Thus, for the real-time prediction of the polarity of the stock price, a Probability Tanh-Independently Recurrent Neural Network (PT-IndRNN)-based classification of the sentiment of the financial text data of Twitter is proposed to solve this problem. Primarily, by employing the corresponding API, the real-time financial data and Twitter data are extracted and stored in the MongoDB database using Apache Flume. This stored data with the historical big datasets are taken and pre-processed. Next, by deploying the proposed Hadoop Distributed File System (HDFS) clustering, the pre-processed stock market data and Twitter data in real-time, as well as the historical dataset, are combined separately. After that, the features are extracted from the clustered sentences. Then, by utilizing the Senti Word Net, the sentences chosen using Linear Scaling-Dwarf Mongoose Optimization Algorithm (LS-DMOA) are converted to negative and positive scores. In the end, the sentiment of the financial texts is classified by the PTh-Ind RNN, which is proved by obtaining reliable result values.