Structuring of Unstructured Data from Heterogeneous Sources

Abstract

Objectives: To develop a new data gathering processing under Big Data Perspectives. To convert unstructured text data into structured format by not missing out any text data available. Methods: The unstructured data is preprocessed using modified stemming and tokenization. From the stemming output, the proposed Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram features are derived. Unstructured data is considered from multiple sources like twitter, consumer complaints and news blog. Findings: The proposed model with extant TF-IDF features has exposed relatively high Mean Average Error (MAE) value which is 1.4325 when compared to the proposed model without optimization to be 0.5197. Novelty: The novelty of the research work is of the stemming process where dictionary checking process is added and the improved feature extraction, interclass dispersion coefficient is computed in TF-IDF features.

Analyzing the Impact of Online Learning on Higher Education: A Text Analytics Approach

“…In this article, we will employ text analytics, social media analytics, and web analytics to analyze students' progress and behavior based on text data [1], [2]. Text analytics enables the extraction of valuable information from a corpus of text, such as students' emails, performance, grades, and more, utilizing computational techniques [3].…”

“…Consequently, a concerning trend of student attrition and diminished academic performance has emerged, posing formidable challenges to Bloom's educational mission [1]. Our objective is to conduct an analysis by gathering unstructured text data from multiple sources to elucidate the underlying issues at Bloom [2]. By facilitating better decision-making processes, we endeavor to enhance the institution's academic outcomes and bolster its overall performance.…”