From Words to Meaning to Insight Julia Cretchley & Mike Neal

 Content Analysis  What is Leximancer?  Steps to your first analysis  In-depth Leximancer Outline

 Leximancer is a software tool designed for analyzing natural language text data  Uses statistics-based algorithms Initial analysis in minutes  Automatically analyzes a text collection User can direct search, add, remove, merge terms  Extracts semantic (meaning) and relational information (more later)  Outputs include concept map, network cloud, quantitative data, concept thesaurus What Is Leximancer?

Leximancer Overview Text

"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. ” What is this text about? (one main topic) Let’s Look at Some Text

 Terms around a word indicate its meaning  Word associations discover concepts; language independent  Leximancer concept: A group of related words that travel together in the text Evidence words include synonyms and adjectives  They begin as seed words for coding and evolve to a thesaurus word-like, Name-like (proper nouns), and compounds (United States) Concept Extraction

 A few things to note... Several concepts may be in a single sentence Concept may span multiple sentences Adjustable resolution (default: 2 sentences) Stop lists remove common words (the, and)  Algorithms Threshold of evidence words for a concept must be present to be coded in a block of text Concept can be coded with evidence words, even if the actual seed word (printer) is not present Concept Extraction cont

"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 pages. We have tried other machines in the past, but have not found an alternative that works better for us. ” Leximancer divides into two sentence units (configurable) Concept Extraction Units of Resolution

"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.” printer concept: paper concept: laser 500, toner, machine, rinting pages, crumpled, jammed printer Laser 500 toner machine machines rinting paper jammed crumpled paper pages Concept Extraction Units of Resolution

 Semantic meaning created through conceptual analysis Presence and frequency of words, phrases Co-occurrence of words make a concept Explicit and implicit concepts identified ( tsunami and earthquake imply Japan)  Relationships created through concept co- occurrence Semantic and Relational Analysis

 Themes Collection of related concepts in close proximity on the map Theme name is most prominent concept  Concept map display Size of dots means frequency of occurrence Line between concepts show relationships Map proximity is by shared friends links (LinkedIn)  Concept map becomes interface to explore underlying text Themes and Concept Map

Laser 500 machine toner rinting Concept and Theme Creation printer pages crumpled jammed Evidence words (thesaurus) Concepts 2 co-occurrences of printer and paper paper

Additional Features  Thesaurus (coding dictionary) automatically generated No manual coding required Profiling and directed coding supported  Analyst can seed their own terms  Sentiment lens feature for affective analysis  Discourse analysis of speakers supported  Survey data analysis supported

 Automated, statistical approach How do you do this manually? No data management, dictionary creation and updates  User does not have to formulate a coding scheme This saves time, and Avoids introduction of researcher bias (grounded theory)  Nuances, subtleties, distinction in expression Word association approach most likely to identify these  Evidence words with links from Leximancer allows deeper exploration, documentation of findings Key Points Summary

Questions?