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About 10,000 papers for “Computational and Text Analysis Methods”

Information analysis in criminal investigations: methods, challenges, and computational opportunities processing unstructured text

Mads Skipanes, Gianluca Demartini, Katrin Franke et al. · Policing A Journal of Policy and Practice · 2025

Abstract In criminal investigations, information is often overlooked due to the manual analysis of vast amounts of text, and the lack of a framework for information analysis. In this paper, we aim to present such a framework by unveiling an experience-based approach derived from the National Criminal Investigation Service (Kripos) in Norway. This framework comprises categorization, thematization and exploration. Through analysis of 742,135 investigative tasks, we find that the major information sources are Interviews and Digital Evidence. From this, the main contribution of this work is a foundation for bridging our framework to computational methods focusing on unstructured text arising from these sources. We map state-of-the-art computational methods within Artificial Intelligence to facilitate the workflow of the proposed framework.

Cited by 2Open access

Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis

Matthew Andreotta, Robertus Setiawan Aji Nugroho, Mark J. Hurlstone et al. · Behavior Research Methods · 2019

To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (non-negative matrix inter-joint factorization; topic alignment) and qualitative (thematic analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

Cited by 142Open access

Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis

Matthew Andreotta, Robertus Wahyu N. Nugroho, Mark J. Hurlstone et al. · 2019

To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content, without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (Non-Negative Matrix inter-joint Factorization; Topic Alignment) and qualitative (Thematic Analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

Cited by 12Open access

Toward Computational Literature Reviews: Analyzing Academic Theories through Supervised and Unsupervised Text-Analysis Methods

Jaren Haber, Heather A. Haveman, Yinuo Xu · 2024

Literature reviews have become challenging as the number of publications has exploded. We develop a flexible method using computational text methods to review massive online collections of journals. We demonstrate this method by investigating engagement with organizational theories in journal articles archived on JSTOR. We focus on three prominent theories: organizational ecology, resource dependence, and organizational institutionalism. We develop comprehensive, period-specific dictionaries of each theory’s core concepts and apply them to articles in JSTOR’s sociology and management subject areas. We compare conceptual engagement to citations of foundational texts in Web of Science. We trace how these theories’ concepts evolved over time by capturing semantic similarity (within theories) and semantic distinctiveness (between theories). This computational method can simplify and accelerate literature reviews by allowing scholars to find which articles engage most strongly with concepts in specific theories and pinpointing which concepts articles engage with.

Cited by 0Open access

From patterns to meaning: a mixed-methods framework that integrates computational and qualitative text analysis

Guenduez, Ali Asker, Fuchs, Saskia, Mergel, Ines et al. · Open MIND · 2025

Applications of automated text analysis techniques have gained significant attention in recent years. Automated text analysis methods are transforming research by offering valuable tools for robust textual analysis and data-driven theory-building. These methods’ impacts on the rigor of qualitative text analysis remain under-researched. We use an advanced automated text analysis method, structural topic model (STM), to assess whether or not a mixed-methods approach that combines automated with qualitative text analysis improves analytical rigor by focusing on four widely recognized criteria: credibility, transferability, dependability, and confirmability. Based on these criteria, our study shows how the combination of automated text analysis and qualitative text analysis can enhance analytical rigor. Ultimately, our research argues that automated techniques are powerful tools in a mixed-methods framework, effectively complementing qualitative-interpretive analysis without replacing the insights and understanding provided by human researchers. We conclude by discussing the implications for future studies employing this mixed-methods approach.

Cited by 0Open access

Opening Up to Big Data : Computer-Assisted Analysis of Textual Data in Social Sciences

Gregor Wiedemann · Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) · 2013

Two developments in computational text analysis may change the way qualitative data analysis in social sciences is performed: 1. the availability of digital text worth to investigate is growing rapidly, and 2. the improvement of algorithmic information extraction approaches, also called text mining, allows for further bridging the gap between qualitative and quantitative text analysis. The key factor hereby is the inclusion of context into computational linguistic models which extends conventional computational content analysis towards the extraction of meaning. To clarify methodological differences of various computer-assisted text analysis approaches the article suggests a typology from the perspective of a qualitative researcher. This typology shows compatibilities between manual qualitative data analysis methods and computational, rather quantitative approaches for large scale mixed method text analysis designs. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1302231

Cited by 119Open access

Text mining for social science – The state and the future of computational text analysis in sociology

Ana Macanovic · Social Science Research · 2022

The emergence of big data and computational tools has introduced new possibilities for using large-scale textual sources in sociological research. Recent work in sociology of culture, science, and economic sociology has shown how computational text analysis can be used in theory building and testing. This review starts with an introduction of the history of computer-assisted text analysis in sociology and then proceeds to discuss five families of computational methods used in contemporary research. Using exemplary studies, it shows how dictionary methods, semantic and network analysis tools, language models, unsupervised, and supervised machine learning can assist sociologists with different analytical tasks. After presenting recent methodological developments, this review summarizes several important implications of using large datasets and computational methods to infer complex meaning in texts. Finally, it calls researchers from different methodological traditions to adopt text mining tools while remaining mindful of lessons learned from working with conventional data and methods.

Cited by 87Open access

Textual Analysis and Computational Insights into Alisher Navoi's ‘Divan of the AqQoyunlu Admirers’

Tulkin Sultanov, Iroda Ablakulova, Nilufar Halimova et al. · 2024

This abstract analyzes Alisher Navoi's “Divan of the AqQoyunlu Admirers” using computational methods. This investigation uses computer science methods to gain deeper insights and nuanced perspectives from Navoi's complex poetry collections. This research project uses text mining, natural language processing, and computational analysis to identify Navoi's poetry's themes, linguistic patterns, and stylistic elements. Computational tools enable a thorough text analysis, which makes it easier to study Navoi's artistic expression, rhetorical devices, and cultural nuances in the “Divan.” This study seeks to reveal Navoi's poetic verses' hidden connections, recurring motifs, and thematic variations. Algorithms will parse, categorize, and interpret text. It also examines how historical and cultural contexts have shaped poetry. A computational lens will be used to understand the socio-cultural background of the AqQoyunlu period. This investigation used computational analysis to supplement traditional literary analysis by providing quantifiable and structured insights into Navoi's work. This research combines computer science and literature to help readers understand Alisher Navoi's “Divan of the AqQoyunlu Admirers”.

Cited by 3

Computational Text Analysis for Social Science: Model Assumptions and Complexity

David Bamman, Noah A. Smith · 2011

Across many disciplines, interest is increasing in the use of computational text analysis in the service of social science questions. We survey the spectrum of current methods, which lie on two dimensions: (1) computational and statistical model complexity; and (2) domain assumptions. This comparative perspective suggests directions of research to better align new methods with the goals of social scientists. 1 Use cases for computational text analysis in the social sciences The use of computational methods to explore research questions in the social sciences and humanities has boomed over the past several years, as the volume of data capturing human communication (including text, audio, video, etc.) has risen to match the ambitious goal of understanding the behaviors of people and society [1]. Automated content analysis of text, which draws on techniques developed in natural language processing, information retrieval, text mining, and machine learning, should be properly understood as a class of quantitative social science methodologies. Employed techniques range from simple analysis of comparative word frequencies to more complex hierarchical admixture models. As this nascent field grows, it is important to clearly present and characterize the assumptions of techniques currently in use, so that new practitioners can be better informed as to the range of available models. To illustrate the breadth of current applications, we list a sampling of substantive questions and studies that have developed or applied computational text analysis to address them.

Cited by 0

Introduction to the Thematic Issue on Language & Knowledge Engineering

David Pinto, Beatríz Beltrán, Andrés S. Vázquez · Computación y Sistemas · 2020

This thematic issue of Computación y Sistemas (CyS) contains a selection of papers presenting advances into the field of Language & Knowledge Engineering and their applications. with topics covering, among others, machine learning methods for classification of unstructured data, information retrieval, ontologies, semisupervised methods, image analysis, sentiment analysis, text mining, language engineering, named entity recognition, knowledge engineering, video analysis, graph based analysis for text analytics, text clustering, opinion mining, recommendation systems and computational linguistics.

Cited by 0

Additional file 1 of Identifying binary protein-protein interactions from affinity purification mass spectrometry data

Xiao-Fei Zhang, Le Ou-Yang, Xiaohua Hu et al. · Figshare · 2015

Supplementary figures and text. This file provides the supplementary figures (Figures S1-S7) that illustrate the comparative performances and the supplementary text which presents the parameter estimation method, convergence analysis, computational complexity analysis and comparative experiments with other regularization methods. (PDF 406 KB)

Cited by 0Open access

Additional file 1 of Identifying binary protein-protein interactions from affinity purification mass spectrometry data

Zhang, Xiao-Fei, Ou-Yang, Le, Xiaohua Hu et al. · Figshare · 2015

Supplementary figures and text. This file provides the supplementary figures (Figures S1-S7) that illustrate the comparative performances and the supplementary text which presents the parameter estimation method, convergence analysis, computational complexity analysis and comparative experiments with other regularization methods. (PDF 406 KB)

Cited by 0Open access

Additional file 1 of Identifying binary protein-protein interactions from affinity purification mass spectrometry data

Zhang, Xiao-Fei, Ou-Yang, Le, Xiaohua Hu et al. · Figshare · 2015

Supplementary figures and text. This file provides the supplementary figures (Figures S1-S7) that illustrate the comparative performances and the supplementary text which presents the parameter estimation method, convergence analysis, computational complexity analysis and comparative experiments with other regularization methods. (PDF 406 KB)

Cited by 0Open access

Additional file 1 of Identifying binary protein-protein interactions from affinity purification mass spectrometry data

Xiao-Fei Zhang, Le Ou-Yang, Xiaohua Hu et al. · Figshare · 2015

Supplementary figures and text. This file provides the supplementary figures (Figures S1-S7) that illustrate the comparative performances and the supplementary text which presents the parameter estimation method, convergence analysis, computational complexity analysis and comparative experiments with other regularization methods. (PDF 406 KB)

Cited by 0Open access

Text as Big Data: Develop codes of practice for rigorous computational text analysis in energy social science

Finn Müller-Hansen, Max Callaghan, Jan C. Minx · 2023

Augmenting traditional social science methods with computational analysis is crucial if we are to exploit the vast digital archives of text data that have become available over the past two decades. In this journal, Benites-Lazaro et al. (2018) showcase this in an application of topic modeling and other computational methods to an actor-specific examination of changes in policy discourse on ethanol in Brazil and point out methodological promises and challenges. However, their contribution also highlights the need for establishing codes of practice for computational text analysis. In this perspective, we discuss five areas for improvement when treating text as big data in light of guiding principles from computational research – transparency, reproducibility and validation – to facilitate rigorous research practice: (1) full transparency over data collection and corpus construction, (2) comprehensive method descriptions that enable reproducibility by other researchers, (3) application of rigorous model validation procedures, (4) results interpretation based on primary text and clear research design and (5) critical discussion and contextualization of main findings. We conclude that the energy social science community needs to develop codes of practice to build on the promising research within the field of computational text analysis and suggest first steps into this direction.

Cited by 0Open access