A Bibliometric Analysis of Text Mining: Exploring the Use of Natural Language Processing in Social Media Research

Abstract

Natural language processing (NLP) plays a pivotal role in modern life by enabling computers to comprehend, analyze, and respond to human language meaningfully, thereby offering exciting new opportunities. As social media platforms experience a surge in global usage, the imperative to capture and better understand the messages disseminated within these networks becomes increasingly crucial. Moreover, the occurrence of adverse events, such as the emergence of a pandemic or conflicts in various parts of the world, heightens social media users’ inclinations towards these platforms. In this context, this paper aims to explore the scientific literature dedicated to the utilization of NLP in social media research, with the goal of highlighting trends, keywords, and collaborative networks within the authorship that contribute to the proliferation of papers in this field. To achieve this objective, we extracted and analyzed 1852 papers from the ISI Web of Science database. An initial observation reveals a remarkable annual growth rate of 62.18%, underscoring the heightened interest of the academic community in this domain. This paper includes an n-gram analysis and a review of the most cited papers in the extracted database, offering a comprehensive bibliometric analysis. The insights gained from these efforts provide essential perspectives and contribute to identifying pertinent issues in social media analysis addressed through the application of NLP.

Guiding Urban Decision-Making: A Study on Recommender Systems in Smart Cities

“…The top three positions were occupied by China (202 citations), India (91 citations), and the USA (62 citations). The notable engagement of these countries in the scientific community, irrespective of the academic subject, positions them as prominent leaders in bibliometric investigations across various timespans and diverse topics compared to the focus of the present article [25,26,29,30,82,83].…”

“…In terms of sources, the foremost position, according to the number of published papers, was occupied by IEEE Transactions on Industrial Informatics, with three articles. The journal belongs to the IEEE publishing house, internationally recognized in the academic community, as further evidenced by its rank among the top journals in other bibliometric studies conducted on different topics and published in the scientific literature [25,26,[28][29][30][80][81][82][83][84].…”

“…The interface is intuitive and easy to use, and it offers users the possibility to import data in row format in the frequently utilized R tool, Biblioshiny, which places Web of Science in the top of authors' preferences [25,26]. These aspects are further supported by the fact that many researchers who have conducted bibliometric studies have exclusively used the Web of Science database to extract the dataset (interested readers can refer to [25,[27][28][29][30][31]), to the detriment of other competitors, which once again underlines its prominent position within the scientific community.…”

Beyond Industry 4.0: Tracing the Path to Industry 5.0 through Bibliometric Analysis

“…Before conducting the bibliometric analysis, it should be stated that the papers have been extracted from the Clarivate Analytics' Web of Science Core Collection database, formerly known as the ISI Web of Science database and referred to as the "WoS" database in the following [43]. Although other databases could have been used for the dataset extraction, we have opted for the WoS database as it provides a wide coverage for the scientific disciplines included in the database [44][45][46] and is acknowledged throughout the world for its reputation among the scientific community in various fields of research [46][47][48]. Furthermore, as the WoS database provides access to the papers based on a subscription, we need to mention that, when extracting the papers, we have had access to all the indexes offered by the database.…”

A Recommendation System for Prosumers Based on Large Language Models

“…Recommendation systems for prosumers in the context of energy management involve sophisticated algorithms designed to analyze various data points, like energy production, consumption patterns, weather forecasts and market prices, even social media insights and news [ 8 ] to provide personalized advice. These systems aim to optimize energy use, reduce costs and even generate revenue by engaging in energy trading [ 9 ].…”