Text Mining in Big Data Analytics

Abstract

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

Predicting Influential Blogger’s by a Novel, Hybrid and Optimized Case Based Reasoning Approach With Balanced Random Forest Using Imbalanced Data

“…As far as the studies related to blog mining (unstructured data pattern extraction) are concerned, a very recent study has provided insights of the literature in this context so far [17]. As an illustration, the former studies have followed a qualitative approach for blog content analysis based on the gender and language to find out the participation of bloggers in blogging.…”

“…Different studies have used non-linear dimension reduction techniques, but focus on a specific type of blogs such as business blogs. Several solutions in blog mining are static and incapable of handling dynamic, complex, and huge blog data [17]. It indicates that in spite of a number of studies relevant to influential blogger identification (by using labeled data) and several blog mining studies, there is still a need of such adaptive framework which is capable of handling both labeled as well as unstructured (blog text) data simultaneously.…”

“…Also, blog content features has been examined for several purposes such as content analysis, opinion mining etc. [17], however, we are only interested in identifying influential bloggers in this context. Authors have also used blogger features along with blog content features in the context of influential blogger prediction [18].…”

A Combination of Lexicon and Machine Learning Approaches for Sentiment Analysis on Facebook

“…The difficulties to track and measure the unorganized and huge size of data accessible in most social networking sites such as Facebook often hinder work in this area (Prichard et al 2015). Monthly active users of Facebook are approximated to be 1.59 billion (Hassani et al, 2020). Facebook has been used in educational settings as depicted in various studies (Teck et al 2013;Zamani et al 2014;Ortigosa, Martí n, and Carro 2014;Saykili andKumtepe 2019 andBarrot 2018).…”

Network Approach for Visualizing the Evolution of the Research of Cross-lingual Semantic Similarity

“…Text analysis in big data analytics is becoming a powerful tool for processing unstructured text data, analyze it to extract new knowledge and identify meaningful models and correlations hidden in the data. Text mining refers to the extraction of information and implicit patterns previously unknown in automatic or semi-automatic mode from a huge unstructured text data such as natural language texts [5].…”