Analysis of Open Answers to Survey Questions through Interactive Clustering and Theme Extraction

Abstract

is paper describes design principles for and the implementation of Gavagai Explorer-a new application which builds on interactive text clustering to extract themes from topically coherent text sets such as open text answers to surveys or questionnaires. An automated system is quick, consistent, and has full coverage over the study material. A system allows an analyst to analyze more answers in a given time period; provides the same initial results regardless of who does the analysis, reducing the risks of interrater discrepancy; and does not risk miss responses due to fatige or boredom. ese factors reduce the cost and increase the reliability of the service. e most important feature, however, is relieving the human analyst from the frustrating aspects of the coding task, freeing the e ort to the central challenge of understanding themes. Gavagai Explorer is available on-line at h p://explorer.gavagai.se CCS CONCEPTS •Information systems → Clustering; Online analytical processing; •Human-centered computing → User interface design;

Social media in the infertile community—using a text analysis tool to identify the topics of discussion on the multitude of infertility blogs

“…It also enables the analysts to group together responses which refer to the same subject or “topic” as they are called in the software. 36 The tool analyzes texts and helps the analysts by suggesting a list of topics based on meaning. The system uses measures of term specificity in language to assess how specific or general a topic is, and it also ranks the topics according to the prevalence in the texts.…”

A Rubric to Assess Students' Clinical Reasoning When Encountering Virtual Patients

“…In study II a new tool, the Gavagai Explorer, was used to perform a qualitativequantitative content analysis (Espinoza et al, 2018;Karlgren, 2016;Parks et al, 2017). A strength of Gavagai Explorer is that the tool enables content analysis of large amounts of text and structures the content analysis around what was actually expressed by the respondents and thereby avoiding that the analysis wanders off in directions that is not warranted by the raw data.…”

“…This tool was used to conduct meaning-based text analytics that build on text clustering and topic extraction. It allows for a qualitative descriptive analysis combined with quantification of data (Espinoza et al, 2018;Parks, Karlgren, & Stymne, 2017). Gavagai Explorer performs an analysis of the manifest content by automated interactive lexical text clustering, quantifying and calculating occurrence of topics in the text (Espinoza et al, 2018).…”

“…The tool processes the dataset in order to identify common topics according to term specificity in language. Gavagai Explorer also helps the analyst by suggesting a list of topics based on associated termsand sentiment scores, and ranking the topics based on occurrences in the text (Espinoza et al, 2018;Parks et al, 2017). In order to dig deeper into the text data, terms and topics can be ignored or merged.…”

Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics

“…The Gavagai Explorer is a commercially available tool which provides an end-to-end solution for the analysis of unstructured text data (Espinoza et al, 2018). We have in these experiments made use of its components for topic clustering, sentiment analysis, and concept modelling.…”