Machine Learning-Based Text Mining in Social Media for COVID-19

News is new information or information about something that is currently happening, presented in the form of print media, broadcasts, social media or word of mouth to third parties or multiple people. The spread of this news is now very much happening both from social media or messages. However, the problem is that no one can guarantee the truth of the news received. Text mining is the application of data mining concepts and techniques to look for patterns in text, the process of analyzing text to find useful information for a particular purpose. Another definition related to text mining is that text mining is data mining in the form of text where the data source is usually obtained from documents and the goal is to find words that can represent the contents of the document so that an analysis of the connectivity between documents can be carried out. To make it easier to identify news hoax requires a text mining algorithm, using a text mining algorithm can be useful to get real news. Based on the calculations of the TF-IDF algorithm, it can be concluded that the similarity of the meaning of the news whose truth is not yet known is closest to the news ofmerahputih.com with a confidence level of 23.11%.

The availability of massive sets of unstructured data has opened up new opportunities for businesses to gain meaningful insights into what is currently happening in their organization and market place. Unfortunately, unstructured data is often challenging to work with, as it requires specialized toolsets, techniques, knowledge and skills to engage with the data. This study used a systematic review (SLR) process to explore the current and conversant state of affairs in the field of text mining (TM) techniques that exist for the processing of natural language in unstructured text datasets across a broad expanse of applications and domains. A comprehensive literature search yielded 1022 eligible articles from five prominent bibliographic databases, narrowed down to 89 articles for review. Information related to TM techniques, the TM process, applications, challenges and recommendations for the improvement of TM results were extracted and synthesized. Eighteen TM techniques were identified and used to complete a variety of tasks such as data pre-processing, information retrieval, information extraction, text classification, clustering, topic modeling, text summarization and sentiment analysis or opinion mining. No single TM technique may be suitable for all text representation and extraction requirements. Therefore, context and applicability are key factors for TM technique selection.KeywordsText miningText mining techniquesUnstructured dataKnowledge discovery

Sentiment analysis (SA) or opinion mining or emotion AI is an on-going field which refers to the use of Natural Language Processing (NLP), analysis of text and is utilized to extract quantify and is used to study the emotional states from a given piece of information or text data set. It is an area that continues to be currently in progress in field of text mining. Sentiment analysis is utilized in many corporations for review of products, comments from social media and from a small amount of it is utilized to check whether or not the text is positive, negative or neutral. Throughout this research work we wish to adopt rule- based approaches which defines a set of rules and inputs like Classic NLP techniques, stemming, tokenization, a region of speech tagging and parsing of machine learning for sentiment analysis which is going to be implemented by most advanced python language.

The Special Interest Group (SIG) on Text Mining (or BioLINK — Biological Literature, Information and Knowledge; http://www.pdg.cnb.uam.es/BioLINK/) was created to address the need for communication and interchange of ideas in the field of text mining and information extraction applied to biology and biomedicine. Information extraction (IE) is an outgrowth of work in automated natural language processing, which began in the 1950s with work on transformational grammar by Zellig Harris [5,6] and later Noam Chomsky [3,4]. Information extraction technology made rapid progress starting in the late 1980s, thanks to a series of conferences focused on evaluation of IE: the Message Understanding Conferences [1]. There is also a long history of research on applications in medicine. Applications to the medical field focus on two distinct sub-problems: improved access to the medical literature and extraction of information from patient records. Despite these successes in other fields, natural language processing (NLP) techniques were not introduced in biology until the late 1990s. Even today, there are two distinct groups: on the one hand, researchers with a background in computer science, and on the other hand, their colleagues with a background in the life sciences, with only limited interaction between the two groups. To improve this situation, the BioLINK group holds regular open meetings to bring together researchers developing text data mining tools and related language processing methods to manage the information explosion in the biomedical field. They include invited and contributed papers, with a focus on developing shared infrastructure (tools, corpora, ontologies) and challenge evaluations, in the style of the KDD Challenge Cups [2]. This year, the BioLINK SIG meeting focused on resources and tools for text mining, with special emphasis on the evaluation of these tools. Speakers from the following areas were invited:

Gigantic amount of content is generated every second on the web. Blog posts, tweets, videos, uploaded images maintain the rich and increasing content of the social media. Great amount of information is obtained from social media through text mining techniques which are being improved and developed continuously. Due to the increasing variety of social media, data is flooding into every business and techniques of gathering information from unstructured data becomes a challenge. Processing and analyzing of large collections of social media text is difficult by using traditional single computer methods. Fortunately, with each passing day, the development and improvements in Big Data infrastructure are promising and the analysis capabilities are also increasing. This infrastructure supports language independent researches on text mining. There can be diversified searches with language-specific definitions and assumptions. Since many of the studies on social media text mining analysis focus on English language, this paper would help enhance text mining efficiently in Turkish language which is a word order free agglutinative language. The linguistic aspects of Turkish language make text mining complicated. Suffixes can change the word meanings easily. Word stemming becomes elaborated due to indigenous letters. The intent of this article is to demonstrate the issues, methods and applications of qualitative approaches that enable text mining in Turkish language considering its compelling features with covering preprocessing, feature selection and topic clustering on text mining considering Turkish linguistic features.

Social media is a remarkable outcome of Web 2.0 technology, which is very popular among the Internet users. The general public is using social media as a communication media to fulfil their information requirements on various occasions such as disaster communication, health communication, marketing products and services and political campaigns. However, the openness of social media provides a great platform for misinformation sharing which is a very common problem in social media communication. Hence, the main objectives of this study are to analyse the publication year and total citation count of publications on misinformation on social media and to identify the main disciplines of misinformation studies on social media using the text mining technique. The primary data of this study were extracted from the Web of Science database using the keywords; social media, misinformation, disinformation and fake news on 16th April 2020. The WoS provided 62 search results and all 62 articles were considered in this study. The title of the article, journal, published year, total citation, abstract, author keywords, keywords plus, Web of Science categories, and research areas were extracted from WoS database. The text mining was done manually. According to the results, scholarly publications on misinformation on social media were first published in the year 2012. Scholarly publications were categorized into 10 main categories; Information, Media, Medical information, Social Science, Communication, Health information, Computer science, Other Sciences, Engineering and Management and Finance. The medical information subject area is covering vast varieties of research areas than the other main subject areas.

Information could be power if most technological approach engaged in the era of IT. Web based text mining is one of the approach that could be analyzed in many ways through soft-computing methods and techniques. The analytical result has shown that different methods of text mining have several advantages with the gap of knowledge that is required to improve. This paper explores the performance of various methods and its impact on specific text mining field such as web based financial text analysis for stock prediction. Key research area of financial text mining is becoming one of the potential research field based on the source of online news, forums, blogs or social media.

Concept for the identification of product innovation potentials by the application of text mining

This study combines text mining and content analysis to examine social media content from the top 20 engineers on Twitter, as well as frequent engineering communication topics on Quora and Reddit, to explore the following: (1) What are important communication skills on social media? (2) What rhetorical strategies do they use communicate information on social media, given the media’s limitations? (3) What topics do engineers most commonly use to engage the public? (4) What topics are associated with “reach” (or distribution and reception of messages) on social media? Findings suggest that appeals and topics that engineers use on social media index personal, technical and professional themes, from technology info and link citations to art, culture, politics, and other modes. Social media blur public, private, and professional spheres of exchange. Public engagement creates heterogeneous networks of topics of interest and practice with diverse vectors of engagement. Interactions between soft skills such as reading, writing, listening and speaking characterize engineering’s multidisciplinary workplaces. Additional important domains (e.g., communication environments, visuals literacy, and interpersonal ethos) emerged. Recommendations for practice and teaching include extending coverage of topics in engineering communication education, providing workplace training on social media, and revising communication policies on the job.

With growing scientific evidence showing the harmful impact of air pollution on the environment and individuals’ health in modern societies, public concern about air pollution has become a central focus of the development of air pollution prevention policy. Past research has shown that social media is a useful tool for collecting data about public opinion and conducting analysis of air pollution. In contrast to statistical sampling based on survey approaches, data retrieved from social media can provide direct information about behavior and capture long-term data being generated by the public. However, there is a lack of studies on how to mine social media to gain valuable insights into the public’s pro-environmental behavior. Therefore, research is needed to integrate information retrieved from social media sites into an established theoretical framework on environmental behaviors. Thus, the aim of this paper is to construct a theoretical model by integrating social media mining into a value-belief-norm model of public concerns about air pollution. We propose a hybrid method that integrates text mining, topic modeling, hierarchical cluster analysis, and partial least squares structural equation modelling (PLS-SEM). We retrieved data regarding public concerns about air pollution from social media sites. We classified the topics using hierarchical cluster analysis and interpreted the results in terms of the value-belief-norm theoretical framework, which encompasses egoistic concerns, altruistic concerns, biospheric concerns, and adaptation strategies regarding air pollution. Then, we used PLS-SEM to confirm the causal relationships and the effects of mediation. An empirical study based on the concerns of Taiwanese social media users about air pollution was used to demonstrate the feasibility of the proposed framework in general and to examine gender differences in particular. Based on the results of the empirical studies, we confirmed the robust effects of egoistic, altruistic, and biospheric concerns of public impact on adaptation strategies. Additionally, we found that gender differences can moderate the causal relationship between egoistic concerns, altruistic concerns, and adaptation strategies. These results demonstrate the effectiveness of enhancing perceptions of air pollution and environmental sustainability by the public. The results of the analysis can serve as a basis for environmental policy and environmental education strategies.

IntroductionSince the Coronavirus disease 2019 (COVID-19), public safety measures, including social distancing and school closures, have been implemented, precipitating psychological difficulties and heightened online activities for adolescents. However, studies examining the impact of the pandemic on adolescent mental health and their coping strategies in Asian countries are limited. Further, most studies have used survey measures to capture mental health challenges so far. Accordingly, this study aimed to examine the psychological challenges South Korean adolescents experienced and their coping strategies during the pandemic using the Natural Language Processing (NLP) and Text mining (TM) technique on adolescents’ social media texts/posts.MethodsThe data were gathered from social media texts/posts such as online communities, Twitter, and personal blogs from January 1, 2019, to October 31, 2021. The 12,520,250 texts containing keywords related to adolescents’ common psychological difficulties reported during the pandemic, including self-harm, Attention-Deficit/Hyperactivity Disorders (ADHD), and insomnia, were analyzed by TM, NLP using information extraction, co-occurrence and sentiment analysis. The monthly frequency of the keywords and their associated words was also analyzed to understand the time trend.ResultsAdolescents used the word “self-harm” in their social media texts more frequently during the second wave of COVID-19 (August to September 2020). “Friends” was the most associated word with “self-harm.” While the frequency of texts with “Insomnia” stayed constant throughout the pandemic, the word “ADHD” was increasingly mentioned in social media. ADHD and insomnia were most frequently associated with ADHD medications and sleeping pills, respectively. Friends were generally associated with positive words, while parents were associated with negative words.ConclusionDuring COVID-19, Korean adolescents often expressed their psychological challenges on social media platforms. However, their coping strategies seemed less efficient to help with their difficulties, warranting strategies to support them in the prolonged pandemic era. For example, Korean adolescents shared psychological challenges such as self-harm with friends rather than their parents. They considered using medicine (e.g., sleeping pills and ADHD medication) as coping strategies for sleep and attention problems.

The field of Text Mining has evolved over the past years to analyze textual resources. However, it can be used in several other applications. In this research, we are particularly interested in performing text mining techniques on audio materials after translating them into texts in order to detect the speakers' emotions. We describe our overall methodology and present our experimental results. In particular, we focus on the different features selection and classification methods used. Our results show interesting conclusions opening up new horizons in the field, and suggest an emergence of promising future work yet to be discovered.

This research article attempts to classify the distinguish authorship problem of three existing Tamil scholars during the Indian independence period, namely, Mahakavi Bharathiar (MB), Subramaniya Iyer (SI) and T.V. Kalyanasundaranar (TVK), using expectation maximisation (EM) algorithm method. The three trendy scholars, as stated above, had written a good number of articles on the India's Freedom Movement during the pre-independence period and published in the magazine India, in the year 1906, is considered for this study. The database consists of 36 articles of blocks of MB, 31 blocks of articles of TVK, 38 blocks of articles of MB, each block having many sentences and 19 function words parameters were used. In the above point of view, this study attempts to identify three authorship of three authors using their stylistic traits and classification of articles of MB, TVK and SI using EM algorithm model. EM algorithm has been used newly as a modern tool and attracted broad research in various disciplines including text mining. Application of EM algorithm has increased considerably in areas of pattern recognition and classification problems in the field of text mining over the last decade.

Media analysis (MA) is an evolving area of research in the field of text mining and an important research area for intelligent media analytics. The fundamental purpose of MA is to obtain valuable insights that help to improve many different areas of business, and ultimately customer experience, through the computational treatment of opinions, sentiments, and subjectivity on mostly highly subjective text types. These texts can come from social media, the internet, and news articles with clearly defined and unique targets. Additionally, MA-related fields include emotion, irony, and hate speech detection, which are usually tackled independently from one another without leveraging the contextual similarity between them, mainly attributed to the lack of annotated datasets. In this paper, we present a unified framework to the complete intelligent media analysis, where we propose a shared parameter layer architecture with a joint learning approach that takes advantage of each separate task for the classification of sentiments, emotions, irony, and hate speech in texts. The proposed approach was evaluated on Greek expert-annotated texts from social media posts, news articles, and internet articles such as blog posts and opinion pieces. The results show that this joint classification approach improves the classification effectiveness of each task in terms of the micro-averaged F1-score.

Depression is a common mental disorder and becomes the leading cause of mental disability worldwide. World health organizations (who) in 2021 reported that nearly 700,000 people die due to suicide yearly. Delays in diagnoses or treatments, inaccuracies, missed diagnoses and therapies for depression are common issues. The possibility of social media as a tool for early depression intervention services to predict depression is examined as people often posts about their daily life on social media platforms. This is considered as a help-seeking behaviour because people who suffers from depression may have different intention to get support through their social network connection. By analysing the language cues in social media posts, machine learning models based on text analytics may be developed to provide an individual with information into his or her mental health earlier than conventional approach. Therefore, the aim of this study is to analyse the text data related to depression extracted from the social media posts to identify the cues or features of depressive behaviour in order to build an algorithm that can effectively predict depression. This study used the cross-industry process for data mining (crisp-dm) methodology for developing the depression detection model. The identified cues from the text analytics are presented as wordcloud. The results show that depressive people tend to have excessive sleep and those who are given a heavy workload often linked to negative emotions felt, such as anger and fear. While, the positive wordcloud include soothing words such as "thank", "love", "fun", "happy" and "game". These cues provide inputs that may be useful to assess individuals with and without depression on social media and can be further explored to develop the depression detection model which will be helpful to physicians and psychiatrists in diagnosing mental diseases and analysing patient behaviour.