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NetMapper · Academic & education

NetMapper, Academic Version

Academic pricing on NetMapper for faculty, researchers, university students and workshop attendees.

NetMapper is a text mining tool that extracts networks from texts

NetMapper extracts semantic networks and meta-networks, supports user-provided thesauri and delete lists, and comes with universal thesauri and delete lists for over 60 languages. NetMapper takes texts as input and outputs DyNetML for analysis in ORA-PRO.

What you can do with NetMapper

  • Turn documents into data. Convert news articles, reports, blogs, transcripts and social media posts into networks you can measure and compare.
  • See who and what is connected. Extract the people, organizations, places, ideas and events in a body of text and the relationships between them.
  • Spot themes and narratives. Reveal the concepts that dominate a discussion and how they link together.
  • Compare sources and time periods. Track how coverage, framing and messaging change across outlets, authors or months.
  • Work across languages. Analyze material in over 60 languages with the built-in thesauri and delete lists, or tailor them with your own.
  • Go straight to analysis. Send results to ORA-PRO as DyNetML for network analysis, visualization and forecasting.

What's included

  • 64-bit Mac (MacOS 10.4 or later) or 64-bit Windows 7/10/11 executable
  • Extracts semantic networks and meta-networks from texts
  • Supports user-provided thesauri and delete lists
  • Universal thesauri and delete lists for over 60 languages
  • Takes texts as input and outputs DyNetML for ORA-PRO
  • 1 Year Support Agreement

System requirements

Windows64-bit Windows 7, 10 or 11
Mac64-bit MacOS 10.4 or later
LicenseNetMapper 64-bit, 1 Software License · NetMapper License Agreement
Support1 Year Support Agreement included; additional years $140 USD each
Guides & videosNetMapper training guides and videos

Classroom & distribution

Classroom LicenseUsing NetMapper in a class? Email info@netanomics.com for details about a Classroom License.
DistributorsInterested in distributing NetMapper? Reach out through our contact page or email info@netanomics.com.

More about NetMapper

How NetMapper works

A closer look at NetMapper’s extraction, cleaning and analysis.

Modes of operation

  • Semantic networks: concepts linked by how often they appear together.
  • Meta-networks: concepts sorted into categories such as agents, organizations, locations, events, tasks, resources, knowledge and beliefs.
  • Concept frequency: counts per document for content analysis.
  • Feature extraction: counts, properties such as reading level, and special features such as abusive terms.
  • Sentiment and CUES: sentiment around your keywords, and subconscious tells about the author’s emotional state.

Inputs and outputs

  • Input: raw .txt files, PDF files (images removed automatically), JSON, CSV and TSV.
  • Output: DyNetML, the dynamic version of GraphML read by ORA, and TSV files with one row per document and one column per feature.

How networks are extracted

NetMapper combines computational linguistics, machine learning and thesauri. Delete and thesaurus lists are applied first, then concepts that appear within the same window (two sentences by default) are linked. Link weights are the number of times two concepts appear together, summed across all texts.

Cleaning built in

  • Removes stop words, connectors, numbers, time words and excess characters with delete lists
  • Corrects common English typos and converts British to American spelling
  • Identifies and translates emoji and emoticons
  • Extracts phone numbers

Languages

Language is detected automatically and can be overridden. Universal thesauri and delete lists cover over 60 languages, and light translation covers over 3,000 words in over 40 languages, from Arabic and Chinese to Russian, Ukrainian and Urdu.

Sentiment in context

Supply a list of keywords and NetMapper scores the sentiment around each one in every document. Nearby words matter: negation flips sentiment, modulators weaken or strengthen it, and nearby words with similar or opposite sentiment reinforce or soften it.

CUES

CUES are subconscious tells in a document that signal the author’s emotional state and can evoke an emotion in the reader, such as first-person pronouns, abusive language, or exclusive and inclusive language.

For novices and experts

  • Novice: apply the defaults and go.
  • Advanced: add domain delete lists, thesauri and translation lists, sentiment keywords, special languages and classification levels.