Sentiment Analysis with Text Mining in Contexts of Big Data

2017 ◽  
Vol 13 (3) ◽  
pp. 47-67 ◽  
Author(s):  
Carina Sofia Andrade ◽  
Maribel Yasmina Santos
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Decision Making Process ◽  
Practical Applications ◽  
Information Models ◽  
Evolution Of Technology ◽  
The Common ◽  
Analyse Data

The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.

Sentiment Analysis with Text Mining in Contexts of Big Data

2020 ◽  
pp. 922-942
Author(s):  
Carina Sofia Andrade ◽  
Maribel Yasmina Santos
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
The Internet ◽  
Practical Applications ◽  
Information Models ◽  
Evolution Of Technology ◽  
The Common ◽  
Analyse Data

The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.

Big Data Myth

2019 ◽  
pp. 223-237
Author(s):  
Agata Mardosz-Grabowska
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Qualitative Data ◽  
New Technologies ◽  
Marketing Management ◽  
Individual Experience ◽  
Decision Making Process

Organizations are expected to act rationally; however, mythical thinking is often present among their members. It refers also to myths related to technology. New inventions and technologies are often mythologized in organizations. People do not understand how new technologies work and usually overestimate their possibilities. Also, myths are useful in dealing with ambivalent feelings, such as fears and hopes. The text focuses on the so-called “big data myth” and its impact on the decision-making process in modern marketing management. Mythical thinking related to big data in organizations has been observed both by scholars and practitioners. The aim of the chapter is to discuss the foundation of the myth, its components, and its impact on the decision-making process. Among others, a presence of a “big data myth” may be manifested by over-reliance on data, neglecting biases in the process of data analysis, and undermining the role of other factors, including intuition and individual experience of marketing professionals or qualitative data.

scholarly journals Text Mining for Big Data Analysis in Financial Sector: A Literature Review

2019 ◽  
Vol 11 (5) ◽  
pp. 1277 ◽  
Author(s):  
Mirjana Pejić Bach ◽  
Živko Krstić ◽  
Sanja Seljan ◽  
Lejla Turulja
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Literature Review ◽  
Financial Sector ◽  
Structured Data ◽  
Decision Making Process ◽  
Strong Impact ◽  
Big Data Technologies ◽  
Research Questions ◽  
Data Investigation

Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis.

scholarly journals Personalized Law and Fundamental Rights

2020 ◽  
Vol 1 (4) ◽  
pp. 56-73
Author(s):  
Tembot Z. Misostishkhov
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Legal Education ◽  
Fundamental Rights ◽  
Legal Positivism ◽  
Decision Making Process ◽  
Minimal Risk ◽  
Legal Norms ◽  
The Public

In recent years, scholars have focused increased attention on the idea of personalized law. It suggests the creation and enforcement of individualized legal norms based on the algorithmic processing of data in the similar manner companies personalize their services using Big Data tools. The article aims to define the role and position of personalized law and to evaluate the risks and consequences of personalization in the context of the emerging digital economy. The research analyses the theoretical grounds of personalized law and justifies its interpretation from the perspective of Hart’s legal positivism striking a balance between the sociological facticity of law and normativism. The study reveals the content, essential features of personalized law and defines its concept. The author analyses the correlation of personalized law with fundamental rights, thus evaluating the risks and consequences of personalization. Particularly, the errors of the approximation of a person’s actual will could occur as part of algorithmic decision-making thereby resulting in discrimination. It appears reasonable that at the beginning, algorithmic personalization should cover only those domains which have the minimal risk of the violation of fundamental norms and of intrusion into the field of social debates. The study underscores, that the transparency of the public sector and of the data-based algorithmic decision-making process is crucial in the context of personalized law, but nevertheless could debase its idea due to opportunistic practices. The issues identified during the research lead one to suggest that professionals who have both legal education and expertise in computer sciences would be in demand in the future. Such professionals could perform the role of independent experts and neutral authority monitoring compliance with data subject’s rights.

Sentiment Mining Approaches for Big Data Classification and Clustering

2018 ◽  
pp. 34-63
Author(s):  
Ashok Kumar J ◽  
Abirami S ◽  
Tina Esther Trueman
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Real World ◽  
Unstructured Data ◽  
Real World Problem ◽  
Sentiment Mining ◽  
Big Data Classification ◽  
The Web ◽  
Classification And Clustering

Sentiment analysis is one of the most important applications in the field of text mining. It computes people's opinions, comments, posts, reviews, evaluations, and emotions which are expressed on products, sales, services, individuals, organizations, etc. Nowadays, large amounts of structured and unstructured data are being produced on the web. The categorizing and grouping of these data become a real-world problem. In this chapter, the authors address the current research in this field, issues and the problem of sentiment analysis on Big Data for classification and clustering. It suggests new methods, applications, algorithm extensions of classification and clustering and software tools in the field of sentiment analysis.

scholarly journals Medication-Related Osteonecrosis of Jaws (MRONJ) Prevention and Diagnosis: Italian Consensus Update 2020

2020 ◽  
Vol 17 (16) ◽  
pp. 5998 ◽  
Author(s):  
Giuseppina Campisi ◽  
Rodolfo Mauceri ◽  
Francesco Bertoldo ◽  
Giordana Bettini ◽  
Matteo Biasotto ◽  
...  
Keyword(s):  
At Risk ◽  
Maxillofacial Surgery ◽  
Oral Pathology ◽  
Consensus Conference ◽  
Decision Making Process ◽  
Dental Management ◽  
Patients At Risk ◽  
The Common

The Medication-Related Osteonecrosis of Jaws (MRONJ) diagnosis process and its prevention play a role of great and rising importance, not only on the Quality of Life (QoL) of patients, but also on the decision-making process by the majority of dentists and oral surgeons involved in MRONJ prevention (primary and secondary). The present paper reports the update of the conclusions from the Consensus Conference—held at the Symposium of the Italian Society of Oral Pathology and Medicine (SIPMO) (20 October 2018, Ancona, Italy)—after the newest recommendations (2020) on MRONJ were published by two scientific societies (Italian Societies of Maxillofacial Surgery and Oral Pathology and Medicine, SICMF and SIPMO), written on the inputs of the experts of the Italian Allied Committee on ONJ (IAC-ONJ). The conference focused on the topic of MRONJ, and in particular on the common practices at risk of inappropriateness in MRONJ diagnosis and therapy, as well as on MRONJ prevention and the dental management of patients at risk of MRONJ. It is a matter of cancer and osteometabolic patients that are at risk since being exposed to several drugs with antiresorptive (i.e., bisphosphonates and denosumab) or, more recently, antiangiogenic activities. At the same time, the Conference traced for dentists and oral surgeons some easy applicable indications and procedures to reduce MRONJ onset risk and to diagnose it early. Continuous updating on these issues, so important for the patient community, is recommended.

scholarly journals Big data text analytics: an enabler of knowledge management

2017 ◽  
Vol 21 (1) ◽  
pp. 18-34 ◽  
Author(s):  
Zaheer Khan ◽  
Tim Vorley
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Text Analytics ◽  
Content Type ◽  
Important Means ◽  
Business Functions ◽  
Analyse Data ◽  
Practical Implications

Purpose The purpose of this paper is to examine the role of big data text analytics as an enabler of knowledge management (KM). The paper argues that big data text analytics represents an important means to visualise and analyse data, especially unstructured data, which have the potential to improve KM within organisations. Design/methodology/approach The study uses text analytics to review 196 articles published in two of the leading KM journals – Journal of Knowledge Management and Journal of Knowledge Management Research & Practice – in 2013 and 2014. The text analytics approach is used to process, extract and analyse the 196 papers to identify trends in terms of keywords, topics and keyword/topic clusters to show the utility of big data text analytics. Findings The findings show how big data text analytics can have a key enabler role in KM. Drawing on the 196 articles analysed, the paper shows the power of big data-oriented text analytics tools in supporting KM through the visualisation of data. In this way, the authors highlight the nature and quality of the knowledge generated through this method for efficient KM in developing a competitive advantage. Research limitations/implications The research has important implications concerning the role of big data text analytics in KM, and specifically the nature and quality of knowledge produced using text analytics. The authors use text analytics to exemplify the value of big data in the context of KM and highlight how future studies could develop and extend these findings in different contexts. Practical implications Results contribute to understanding the role of big data text analytics as a means to enhance the effectiveness of KM. The paper provides important insights that can be applied to different business functions, from supply chain management to marketing management to support KM, through the use of big data text analytics. Originality/value The study demonstrates the practical application of the big data tools for data visualisation, and, with it, improving KM.

Sign in / Sign up

Export Citation Format

Share Document