1. From Words to Meaning to Insight Julia Cretchley & Mike Neal

3. Outline  Getting started  Creating projects and loading data  Run the project  Initial results interpretations The Concept Map

4. Getting Started Help Button--> About --> Shows version of Leximancer Manual --> Access PDF Manual Contact--> Starts email

5. Projects Manage Projects--> Create folders under Leximancer Projects to organize your own projects. Create Project--> Create projects in current folder. Interviews--> Double Click to Open Project Panel

6. Planning Projects  Fast, first-cut analysis for pure discovery (grounded theory) 1. Load Data 2. Run steps with no editing or configuration 3. Examine results and explore data  Deliberate, planned analysis 1. Load Data 2. Set up custom configuration (tags, sentiment analysis) 3. Examine results; explore data; modify settings 4. Repeat 2 and 3

7. Project Control Panel Four main interaction areas 1 2 3 4 Current status Configure and option editors Reporting and Exploration Buttons

8. Steps to Analysis  Fast, first-cut analysis for pure discovery (grounded theory) 1. Load Data 2. Run steps with no editing or configuration 3. Examine results and explore data  Deliberate, planned analysis 1. Load Data 2. Set up custom configuration (tags, sentiment analysis) 3. Examine results; explore data; modify settings 4. Repeat 2 and 3

9. Stages: Load Data

10. Load Data  Data formats xls, cvs, tsv for spreadsheet loading pdf, doc, docx, rtf, txt, html, xml, xhtml  Two options 1. Spreadsheet 2. Files and file folders of documents  Tags (briefly...) Organize data into folders or spreadsheet columns (automatic) by date or topic for Dashboard later

11. Stages Run Project

12. What Did Leximancer Just Do?  Split the text into sentences, paragraphs, and documents  Divided the text into blocks of 2 sentences (by default)  Identified Proper Nouns and multi-word (compound) names  Removed non-lexical and weak semantic information (i.e., stop word list)  Determined seed words via most frequent words and relationships  Used seed words to build coding dictionary (i.e., thesaurus)  Use thesaurus to code text and tagged the blocks the concepts they contain  Measured co-occurrence between concepts  Produced concepts, themes, final thesaurus Statistics (frequencies, measurements) Outputs (Dashboard only if configured)  Used seed words to build coding dictionary (i.e., thesaurus)  Use thesaurus to code text and tagged the blocks the concepts they contain  Measured co-occurrence between concepts  Produced concepts, themes, final thesaurus Statistics (frequencies, measurements) Outputs (Dashboard only if configured)

13. View Results  Concept Map and Concept Cloud are key interfaces  Activities analyst typically performs now Understand the initial run and data Explore thesaurus; links to actual data Look for concepts to merge, remove, or make compound Create Dashboard Report, export data; save map  Run analysis again; repeat as necessary

14. Concept Map Colored spheres are Themes Dots are concepts (size matters) Connections shown Control % of concepts % of themes Rotate for better display Controls to toggle concept map, network display, center, zoom, save, export Theme Summary Ranked list Examples more... Concept Summary Ranked list Name-like word-like

15.  Leximancer uses concept frequency and co- occurrence data to compile a matrix of concept co- occurrences You can export this matrix to Excel for your own visualizations  A statistical algorithm is then used to create a two- dimensional concept map based on the matrix  Initially, concepts are dispersed randomly in the map space. Then the relationships between concepts act like attractive forces to guide concepts to their resting places. Concept Map

16. Concept Cloud Concept Relationships highlighted Colors are heat mapped (Themes) Rotate for better view Save Map/Export Image in case of new run

17.  Top Name-like concepts at top (Proper names by capital first letter)  Click name and get ranked list of related concepts  Count is number of times word (concept) appears in entire corpus (2- sentence blocks)  Relevance is most frequent concept (Japan:7010) as 100%. Divide counts by 7010 for percentages. Shows proportionality (representative) relative to each other Concept Tab

18. "We use the laser 500 printer here at the office. We are pretty happy with it. Once there was a leak and all the toner spilled out of the machine, but a technician came out and fixed the problem for us. We still have to top the toner up often. The printer goes through ink quickly and the cartridges are expensive, but we put up with this because it delivers good results reliably. We are pleased with the quality of rinting we get. The laser 500 can batch process, and collate the pages to save us time. Sometimes paper gets jammed in the laser 500. Then we have to open it up to remove the crumpled paper. We have tried other machines in the past, but have not found an alternative that works better for us.” For printer concept: ____ occurrences from Leximancer occurrences by ordinary keyword text search ____ 2 5 Concept Extraction A Test! printer laser 500 toner machine machines rinting

19. Redcross clicked Select a Concept Lines drawn to related concepts Count is number of times concept is mentioned with Redcross. Example donation: 196. Count is number of times concept is mentioned with Redcross. Example donation: 196. So, of all comments about donation, 68% mention Redcross.

20. Thesaurus Concepts here listed in abc order Click concept to see thesaurus: evidence words describing concept. Click concept to see thesaurus: evidence words describing concept. Score is z-score. Higher score is more relevant. Higher relevance value means: -Occur often in sentences containing the concept -Rarely occur in sentences not containing the concept Score is z-score. Higher score is more relevant. Higher relevance value means: -Occur often in sentences containing the concept -Rarely occur in sentences not containing the concept

21. Questions?