Natural Language Processing in Mixed-methods Text Analysis: A Workflow Approach

2022 ◽  
pp. 1-13
Author(s):  
Louisa Parks ◽  
Wim Peters
Keyword(s):  
Natural Language Processing ◽  
Mixed Methods ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis

scholarly journals Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Siyuan Zhao ◽  
Zhiwei Xu ◽  
Limin Liu ◽  
Mengjie Guo ◽  
Jing Yun
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Word Order ◽  
Text Analysis ◽  
Important Application ◽  
Detection Mechanism ◽  
Short Text

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.

An approach to neural network analysis of text information in the economic assessment of companies

2021 ◽  
Vol 20 (8) ◽  
pp. 1574-1594
Author(s):  
Aleksandr R. NEVREDINOV
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Economic Assessment ◽  
Management Decision ◽  
Textual Information ◽  
Financial Condition ◽  
Analysis And Synthesis ◽  
Management Decision Making

Subject. When evaluating enterprises, maximum accuracy and comprehensiveness of analysis are important, although the use of various indicators of organization’s financial condition and external factors provide a sufficiently high accuracy of forecasting. Many researchers are increasingly focusing on the natural language processing to analyze various text sources. This subject is extremely relevant against the needs of companies to quickly and extensively analyze their activities. Objectives. The study aims at exploring the natural language processing methods and sources of textual information about companies that can be used in the analysis, and developing an approach to the analysis of textual information. Methods. The study draws on methods of analysis and synthesis, systematization, formalization, comparative analysis, theoretical and methodological provisions contained in domestic and foreign scientific works on text analysis, including for purposes of company evaluation. Results. I offer and test an approach to using non-numeric indicators for company analysis. The paper presents a unique model, which is created on the basis of existing developments that have shown their effectiveness. I also substantiate the use of this approach to analyze a company’s condition and to include the analysis results in models for overall assessment of the state of companies. Conclusions. The findings improve scientific and practical understanding of techniques for the analysis of companies, the ways of applying text analysis, using machine learning. They can be used to support management decision-making to automate the analysis of their own and other companies in the market, with which they interact.

Leveraging Python to Process Cross-Cultural Temperament Interviews: A Novel Platform for Text Analysis

2020 ◽  
Vol 51 (2) ◽  
pp. 168-181 ◽  
Author(s):  
Joshua J. Underwood ◽  
Cornelia Kirchhoff ◽  
Haven Warwick ◽  
Maria A. Gartstein
Keyword(s):  
Early Childhood ◽  
Natural Language Processing ◽  
Individual Differences ◽  
Natural Language ◽  
Language Processing ◽  
Data Reduction ◽  
Text Analysis ◽  
Cross Cultural ◽  
Two Samples ◽  
Do So

During childhood, parents represent the most commonly used source of their child’s temperament information and, typically, do so by responding to questionnaires. Despite their wide-ranging applications, interviews present notorious data reduction challenges, as quantification of narratives has proven to be a labor-intensive process. However, for the purposes of this study, the labor-intensive nature may have conferred distinct advantages. The present study represents a demonstration project aimed at leveraging emerging technologies for this purpose. Specifically, we used Python natural language processing capabilities to analyze semistructured temperament interviews conducted with U.S. and German mothers of toddlers, expecting to identify differences between these two samples in the frequency of words used to describe individual differences, along with some similarities. Two different word lists were used: (a) a set of German personality words and (b) temperament-related words extracted from the Early Childhood Behavior Questionnaire (ECBQ). Analyses using the German trait word demonstrated that mothers from Germany described their toddlers as significantly more “cheerful” and “careful” compared with U.S. caregivers. According to U.S. mothers, their children were more “independent,” “emotional,” and “timid.” For the ECBQ analysis, German mothers described their children as “calm” and “careful” more often than U.S. mothers. U.S. mothers, however, referred to their children as “upset,” “happy,” and “frustrated” more frequently than German caregivers. The Python code developed herein illustrates this software as a viable research tool for cross-cultural investigations.

scholarly journals Deep learning approach to text analysis for human emotion detection from big data

2022 ◽  
Vol 31 (1) ◽  
pp. 113-126
Author(s):  
Jia Guo
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Question Answering ◽  
Word Embeddings ◽  
Emotion Detection ◽  
Human Emotion

Abstract Emotional recognition has arisen as an essential field of study that can expose a variety of valuable inputs. Emotion can be articulated in several means that can be seen, like speech and facial expressions, written text, and gestures. Emotion recognition in a text document is fundamentally a content-based classification issue, including notions from natural language processing (NLP) and deep learning fields. Hence, in this study, deep learning assisted semantic text analysis (DLSTA) has been proposed for human emotion detection using big data. Emotion detection from textual sources can be done utilizing notions of Natural Language Processing. Word embeddings are extensively utilized for several NLP tasks, like machine translation, sentiment analysis, and question answering. NLP techniques improve the performance of learning-based methods by incorporating the semantic and syntactic features of the text. The numerical outcomes demonstrate that the suggested method achieves an expressively superior quality of human emotion detection rate of 97.22% and the classification accuracy rate of 98.02% with different state-of-the-art methods and can be enhanced by other emotional word embeddings.

scholarly journals Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing

2016 ◽  
Vol 25 (01) ◽  
pp. 224-233 ◽  
Author(s):  
N. Elhadad ◽  
D. Demner-Fushman
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Disease Modeling ◽  
Text Processing ◽  
Healthcare Quality ◽  
Unintended Consequences ◽  
Health Related

Summary Objectives: This paper reviews work over the past two years in Natural Language Processing (NLP) applied to clinical and consumer-generated texts. Methods: We included any application or methodological publication that leverages text to facilitate healthcare and address the health-related needs of consumers and populations. Results: Many important developments in clinical text processing, both foundational and task-oriented, were addressed in community-wide evaluations and discussed in corresponding special issues that are referenced in this review. These focused issues and in-depth reviews of several other active research areas, such as pharmacovigilance and summarization, allowed us to discuss in greater depth disease modeling and predictive analytics using clinical texts, and text analysis in social media for healthcare quality assessment, trends towards online interventions based on rapid analysis of health-related posts, and consumer health question answering, among other issues. Conclusions: Our analysis shows that although clinical NLP continues to advance towards practical applications and more NLP methods are used in large-scale live health information applications, more needs to be done to make NLP use in clinical applications a routine widespread reality. Progress in clinical NLP is mirrored by developments in social media text analysis: the research is moving from capturing trends to addressing individual health-related posts, thus showing potential to become a tool for precision medicine and a valuable addition to the standard healthcare quality evaluation tools.

scholarly journals Neurolinguistic approach to natural language processing with applications to medical text analysis

2008 ◽  
Vol 21 (10) ◽  
pp. 1500-1510 ◽  
Author(s):  
Włodzisław Duch ◽  
Paweł Matykiewicz ◽  
John Pestian
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Medical Text

scholarly journals Further Frontiers in GIS: Extending Spatial Analysis to Textual Sources in Archaeology

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Patricia Murrieta-Flores ◽  
Ian Gregory
Keyword(s):  
Natural Language Processing ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Corpus Linguistics ◽  
Geographic Information ◽  
Processing Techniques

AbstractAlthough the use of Geographic Information Systems (GIS) has a long history in archaeology, spatial technologies have been rarely used to analyse the content of textual collections. A newly developed approach termed Geographic Text Analysis (GTA) is now allowing the semi-automated exploration of large corpora incorporating a combination of Natural Language Processing techniques, Corpus Linguistics, and GIS. In this article we explain the development of GTA, propose possible uses of this methodology in the field of archaeology, and give a summary of the challenges that emerge from this type of analysis.

scholarly journals Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis

2020 ◽  
Vol 10 (6) ◽  
pp. 2157 ◽  
Author(s):  
Xieling Chen ◽  
Haoran Xie ◽  
Gary Cheng ◽  
Leonard K. M. Poon ◽  
Mingming Leng ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Research Field ◽  
Test Analysis ◽  
Clinical Trial Research ◽  
Scientific Outputs ◽  
Automatic Text Analysis

Natural language processing (NLP) is an effective tool for generating structured information from unstructured data, the one that is commonly found in clinical trial texts. Such interdisciplinary research has gradually grown into a flourishing research field with accumulated scientific outputs available. In this study, bibliographical data collected from Web of Science, PubMed, and Scopus databases from 2001 to 2018 had been investigated with the use of three prominent methods, including performance analysis, science mapping, and, particularly, an automatic text analysis approach named structural topic modeling. Topical trend visualization and test analysis were further employed to quantify the effects of the year of publication on topic proportions. Topical diverse distributions across prolific countries/regions and institutions were also visualized and compared. In addition, scientific collaborations between countries/regions, institutions, and authors were also explored using social network analysis. The findings obtained were essential for facilitating the development of the NLP-enhanced clinical trial texts processing, boosting scientific and technological NLP-enhanced clinical trial research, and facilitating inter-country/region and inter-institution collaborations.

Sign in / Sign up

Export Citation Format

Share Document