A TRIAL OF THE THEMATIC GROUPS OF WORDS FOR TEXT MINING

2021 ◽  
Author(s):  
Юлия Михайловна Кузнецова
Keyword(s):  
Text Mining ◽  
Text Analysis ◽  
Social Stress ◽  
Network Communication ◽  
Lexical Frequency

В работе рассматриваются результаты лексико-частотного анализа письменных текстов с использованием специально созданных тематических групп слов русского языка. Выявленная чувствительность к состояниям фрустрации, агрессии и депрессии определяет перспективность их применения в целях мониторинга в сетевом общении признаков развития социального стресса. The paper considers the results of the lexical frequency text analysis via the specially composed thematic groups of Russian words. The revealed sensitivity to the frustration, aggression and depression makes their use promising for monitoring in network communication some signs of social stress arising.

SPCCTDM, a Catalogue for Analysis of Therapeutic Drug Monitoring Related Contents

2013 ◽  
pp. 319-328
Author(s):  
Sven Ulrich ◽  
Pierre Baumann ◽  
Andreas Conca ◽  
Hans-Joachim Kuss ◽  
Viktoria Stieffenhofer ◽  
...  
Keyword(s):  
Drug Therapy ◽  
Therapeutic Drug Monitoring ◽  
Text Mining ◽  
Drug Monitoring ◽  
Text Analysis ◽  
Scientific Evidence ◽  
Plasma Concentrations ◽  
Therapeutic Drug ◽  
Drug Reactions ◽  
First Time

Therapeutic drug monitoring (TDM) has consistently been shown to be useful for optimization of drug therapy. For the first time, a method has been developed for the text analysis of TDM in SPCs in that a catalogue SPC-ContentTDM (SPCCTDM) provides a codification of the content of TDM in SPCs. It consists of six structure-related items (dose, adverse drug reactions, drug interactions, overdose, pregnancy/breast feeding, and pharmacokinetics) according to implicit or explicit references to TDM in paragraphs of the SPC, and four theory-guided items according to the information about ranges of plasma concentrations and a recommendation of TDM in the SPC. The catalogue is regarded as valid for the text analysis of SPCs with respect to TDM. It can be used in the comparison of SPCs, in the comparison with medico-scientific evidence and for the estimation of the perception of TDM in SPCs by the reader. Regarding the approach as a model of text mining, it may be extended for evaluation of other aspects reported in SPCs.

A Survey on Sentiment Analysis Techniques for Twitter

2022 ◽  
pp. 57-90
Author(s):  
Surabhi Verma ◽  
Ankit Kumar Jain
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Analysis Techniques ◽  
Goods And Services ◽  
Text Document ◽  
The Subject ◽  
Over Time

People regularly use social media to express their opinions about a wide variety of topics, goods, and services which make it rich in text mining and sentiment analysis. Sentiment analysis is a form of text analysis determining polarity (positive, negative, or neutral) in text, document, paragraph, or clause. This chapter offers an overview of the subject by examining the proposed algorithms for sentiment analysis on Twitter and briefly explaining them. In addition, the authors also address fields related to monitoring sentiments over time, regional view of views, neutral tweet analysis, sarcasm detection, and various other tasks in this area that have drawn the researchers ' attention to this subject nearby. Within this chapter, all the services used are briefly summarized. The key contribution of this survey is the taxonomy based on the methods suggested and the debate on the theme's recent research developments and related fields.

scholarly journals Text Mining Data from Students to Reveal Meaningful Information for Educators

2021 ◽  
Vol 24 (1) ◽  
pp. 5-30
Author(s):  
Zainab M. AlQenaei ◽  
David E. Monarchi
Keyword(s):  
Text Mining ◽  
Learning Strategies ◽  
Undergraduate Students ◽  
Text Analysis ◽  
Past Research ◽  
Text Data ◽  
Final Grade ◽  
Meaningful Information ◽  
Novel Approach ◽  
Academic Profiles

Academic institutions adopt different advising tools for various objectives. Past research used both numeric and text data to predict students’ performance. Moreover, numerous research projects have been conducted to find different learning strategies and profiles of students. Those strategies of learning together with academic profiles assisted in the advising process. This research proposes an approach to supplement these activities by text mining students’ essays to better understand different students’ profiles across different courses (subjects). Text analysis was performed on 99 essays written by undergraduate students in three different courses. The essays and terms were projected in a 20-dimensional vector space. The 20 dimensions were used as independent variables in a regression analysis to predict a student’s final grade in a course. Further analyses were performed on the dimensions found statistically significant. This study is a preliminary analysis to demonstrate a novel approach of extracting meaningful information by text mining essays written by students to develop an advising tool that can be used by educators.

scholarly journals Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016

2019 ◽  
Vol 15 (2) ◽  
pp. 226
Author(s):  
Wishnu Hardi ◽  
Wisnu Ananta Kusuma ◽  
Sulistyo Basuki
Keyword(s):  
Data Analysis ◽  
Text Mining ◽  
Human Development ◽  
Hierarchical Clustering ◽  
Text Analysis ◽  
Economic Cooperation ◽  
Clustering Method ◽  
Media Release ◽  
Data Objects ◽  
Data Variation

Introduction. The Australian Embassy in Jakarta is storing a wide array of media release document. Analyzing particular and vital patterns of the documents collection is imperative as it will result in new insights and knowledge of significant topic groups of the documents.Methodology. K-Means was used algorithm as a non-hierarchical clustering method which partitioning data objects into clusters. The method works through minimizing data variation within cluster and maximizing data variation between clusters. Data Analysis.  Of the documents issued between 2006 and 2016, 839 documents were examined in order to determine term frequencies and to generate clusters. Evaluation was conducted by nominating an expert to validate the cluster result.Results and discussions. The result showed that there were 57 meaningful terms grouped into 3 clusters. “People to people links”, “economic cooperation”, and “human development” were chosen to represent topics of the Australian Embassy Jakarta media releases from 2006 to 2016.Conclusions. Text mining can be used to cluster topic groups of documents. It provides a more systematic clustering process as the text analysis is conducted through a number of stages with specifically set parameters.  

A Survey of Selected Software Technologies for Text Mining

2009 ◽  
pp. 1164-1181
Author(s):  
Richard S. Segall ◽  
Qingyu Zhang
Keyword(s):  
Data Analysis ◽  
Text Mining ◽  
Text Analysis ◽  
Web Mining ◽  
Qualitative Data ◽  
Future Trends ◽  
Data Preparation ◽  
Software Packages ◽  
Visual Text ◽  
Key Steps

This chapter presents background on text mining, and comparisons and summaries of seven selected software for text mining. The text mining software selected for discussion and comparison in this chapter are: Compare Suite by AKS-Labs, SAS Text Miner, Megaputer Text Analyst, Visual Text by Text Analysis International, Inc. (TextAI), Magaputer PolyAnalyst, WordStat by Provalis Research, and SPSS Clementine. This chapter not only discusses unique features of these text mining software packages but also compares the features offered by each in the following key steps in analyzing unstructured qualitative data: data preparation, data analysis, and result reporting. A brief discussion of Web mining and its software are also presented, as well as conclusions and future trends.

How Korean universities portray themselves in the global marketplace: text-mining analysis of university president's messages

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Soo Jeung Lee ◽  
Soowon Park
Keyword(s):  
Text Mining ◽  
Text Analysis ◽  
Design Methodology ◽  
Global Marketplace ◽  
Content Type ◽  
University Websites ◽  
Education Environment ◽  
Word Clouds ◽  
Extract Information ◽  
Korean Universities

PurposeThis study aims to examine university president's messages (PMs) on Korean university websites to analyze how Korean universities present their image and position themselves in the global marketplace.Design/methodology/approachAssuming that visions, missions and strategies might vary depending on the characteristics of a university, the study analyzed PMs according to university type: research, teaching and technology. The authors applied text analysis to 105 Korean universities' PMs to understand the images they project. The authors also used text mining on the PMs to examine the frequencies of keywords, to create word clouds, to investigate the keywords' degrees of centrality and to conduct sentiment analysis.FindingsThe findings show that Korean universities' PMs project hybrid images, simultaneously portraying the universities as public institutes that produce public goods and as globally competitive strategic actors. In addition, while Korean university PMs explicitly position the universities as education-oriented, they nonetheless reveal that the universities pursue both research-oriented and education-oriented goals.Originality/valueThis is the study to examine PMs using text mining with Python to extract information and reveal hidden meanings regarding how universities portray themselves on their websites. Highlighting current challenges faced by universities, this article argues for continued discussion on their societal roles and their strategies for positioning themselves in today's globalized and marketized higher education environment.

scholarly journals Identifying Themes and Patterns on Management of Horticultural Innovations with an Automated Text Analysis

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1103
Author(s):  
Daniela Spina ◽  
Gabriella Vindigni ◽  
Biagio Pecorino ◽  
Gioacchino Pappalardo ◽  
Mario D’Amico ◽  
...  
Keyword(s):  
Text Mining ◽  
Text Analysis ◽  
Qualitative Data ◽  
Urban System ◽  
Software Tool ◽  
Broad Perspective ◽  
Data Coding ◽  
Pooled Data ◽  
Holistic Understanding ◽  
Automated Text Analysis

This research provides an overview on horticulture innovations in the last decade through a literature review and the use of a computer qualitative data analysis. We used Leximancer text mining software to identify concepts, themes and pathways linked with horticulture innovations. The software tool enabled us to “zoom out” to gain a broad perspective of the pooled data, and it indicated which studies clustered around the dominant topic. It displays the extracted information in a visual form, to wit, an interactive concept map, which summaries the interconnected themes and demonstrates any interdependencies. The text mining analysis revealed that the themes strongly related to “innovation” are “water”, “urban”, “system”, “countries” and “technology”. The outputs identified have been interpreted to discover meaning from the content analysis, since the software can facilitate a comprehensive and transparent data coding but cannot replace researcher’s interpretive work. Furthermore, we focused on the diffusion and the barriers for the spread of innovation, pointing out the differences about developing and advanced countries. This analysis allows the researcher to have a holistic understanding of the examination area and could lead to further studies.

scholarly journals A Study of the Influence of Online Information on the Changes in the Warsaw Stock Exchange Indexes

2018 ◽  
Vol 3 (335) ◽  
pp. 123-138
Author(s):  
Piotr Młodzianowski
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Stock Exchange ◽  
Online Information ◽  
Analysis Process ◽  
Study Results ◽  
Warsaw Stock Exchange

The article presents the results of a study on the influence of online information originating from financial websites on changes in the Warsaw Stock Exchange indexes. The first part is theoretical. It describes the issue of text mining and sentiment analysis and their use in the text analysis process. The next part of the article describes the characteristics of the study. A selection was made of Polish financial websites that may trigger reactions from investors on the Warsaw Stock Exchange. Words occurring on the analysed websites were selected and put into classes. Then the relation between changes in WSE indexes and the frequency of appearance of individual words within the classes was analysed. The last part of the article presents the study results, discusses the possibilities of using them and indicates further areas for research.

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