Text Mining: Techniques, Applications, and Challenges

Torre, C. Justicia de la; Sánchez, Daniel; Blanco, Ignacio J.; Martín-Bautista, María J.

doi:10.1142/s0218488518500265

Cited by 30 publications

(18 citation statements)

References 61 publications

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“…Text mining technology is an essential analytical tool and method in big data analytics [16]. Text mining is based on advanced statistics, machine learning, and linguistics techniques.…”

Section: Text Mining Techniquesmentioning

confidence: 99%

[Retracted] Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology

Pei

2022

Journal of Environmental and Public Health

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The company is an essential organization in modern society, and the company has transformed from a purely economic organization to a corporate citizen that realizes economic responsibility and practices social responsibility at the same time. It is only by constructing a legal system of corporate social responsibility that companies can take social responsibility on the track of the legal system, realize the company’s mission of the times, and achieve a win-win situation for both the company and society. This paper used the LDA and text clustering methods to analyze existing legal texts. It obtained the theme and text clustering results, thus proposing five aspects of the legal system construction framework to guide the corporate social responsibility legal system, which has pioneering significance.

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“…Text mining technology is an essential analytical tool and method in big data analytics [16]. Text mining is based on advanced statistics, machine learning, and linguistics techniques.…”

Section: Text Mining Techniquesmentioning

confidence: 99%

[Retracted] Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology

Pei

2022

Journal of Environmental and Public Health

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“…In the recent debates, text mining definition and algorithms involved in its implementation has been equivocally discussed (Hudaefi et al , 2021; Hudaefi and Badeges, 2021; Jiang et al , 2013; Villarroel Ordenes and Zhang, 2019). However, it has been understood as a specialised form of data mining to extract information and discover knowledge from vast amounts of unstructured textual data (Cheerkoot-Jalim and Khedo, 2020; Jiang et al , 2014; Justicia De La Torre et al , 2018; Usai et al , 2018). Researchers have used data mining in the area of computer science (Liu, 2012; Tsoumakas and Katakis, 2007), engineering (Hu and Liu, 2004; Medhat et al , 2014), mathematics (Berry et al , 2007; Zhao et al , 2011), social sciences (Goh et al , 2013; Liu, 2012), business and economics (Choudhury et al , 2020; Hudaefi and Badeges, 2021; Kinateder et al , 2021).…”

Section: Methodsmentioning

confidence: 99%

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Hassan

Hudaefi

Caraka

2021

SEF

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Purpose This paper aims to explore netizen’s opinions on cryptocurrency under the lens of emotion theory and lexicon sentiments analysis via machine learning. Design/methodology/approach An automated Web-scrapping via RStudio is performed to collect the data of 15,000 tweets on cryptocurrency. Sentiment lexicon analysis is done via machine learning to evaluate the emotion score of the sample. The types of emotion tested are anger, anticipation, disgust, fear, joy, sadness, surprise, trust and the two primary sentiments, i.e. negative and positive. Findings The supervised machine learning discovers a total score of 53,077 sentiments from the sampled 15,000 tweets. This score is from the artificial intelligence evaluation of eight emotions, i.e. anger (2%), anticipation (18%), disgust (1%), fear (3%), joy (15%), sadness (3%), surprise (7%), trust (15%) and the two sentiments, i.e. negative (4%) and positive (33%). The result indicates that the sample primarily contains positive sentiments. This finding is theoretically significant to measure the emotion theory on the sampled tweets that can best explain the social implications of the cryptocurrency phenomenon. Research limitations/implications This work is limited to evaluate the sampled tweets’ sentiment scores to explain the social implication of cryptocurrency. Practical implications The finding is necessary to explain the recent phenomenon of cryptocurrency. The positive sentiment may describe the increase in investment in the decentralised finance market. Meanwhile, the anticipation emotion may illustrate the public’s reaction to the bubble prices of cryptocurrencies. Social implications Previous studies find that the social signals, e.g. word-of-mouth, netizens’ opinions, among others, affect the cryptocurrencies’ movement prices. This paper helps explain the social implications of such dynamic of pricing via sentiment analysis. Originality/value This study contributes to theoretically explain the implications of the cryptocurrency phenomenon under the emotion theory. Specifically, this study shows how supervised machine learning can measure the emotion theory from data tweets to explain the implications of cryptocurrencies.

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“…This data mining technique where the input data is in the form of unstructured text has enabled an automated review of a large number of published papers. The technique has become essential in support of knowledge discovery in texts (Justicia De La Torre et al, 2018) and it is commonly used to extract useful hidden information and patterns in a text (Kaur & Chopra, 2016). In this particular case, an insight into the occurrences of certain keywords related to the domain of a smart city was investigated from research perspectives of information systems and management.…”

Section: Methodsmentioning

confidence: 99%

Exploring Smart City Research From Information Systems and Management Perspectives

Jadrić¹,

Croatia²

2020

Fourth International Scientific Conference ITEMA Recent Advances in Information Technology, Tourism, Economics, Management and

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Research in the smart city domain is characterised by distinct multidisciplinarity. The reason for this is the broadness of the domain, classified into six key categories: smart governance, smart people, smart living, smart mobility, smart economy, and smart environment, all focal points of research in separate scientific fields. Also, many researchers argue about the best approach and steps in the development of smart cities highlighting different technological, economic, or sociological aspects of research. This paper aims to explore and clarify the differences in smart city research from two different perspectives - information systems and management. Abstracts from almost 5.000 papers from the WoS database and more than 7.000 papers from the Scopus database were downloaded and analysed. Publications categorised into two perspectives were then analysed descriptively, including data about the number of papers, year of publication, and country of publishing. Furthermore, automated text mining procedure was performed for additional interpretation of attributes and occurrences from the two observed perspectives. The use of six smart city categories as keywords within each set was also analysed and visualised. The results indicate clear differences in both research approaches and research subjects between the two perspectives.

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Text Mining: Techniques, Applications, and Challenges

Cited by 30 publications

References 61 publications

[Retracted] Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology

[Retracted] Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Exploring Smart City Research From Information Systems and Management Perspectives

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