In this 3-part series, participants will be introduced to common text analysis methods and tools.

• Session 1: Basics of Text Analysis with Voyant – Thursday, February 5
• Session 2: Sentiment Analysis with Digital Scholar Lab – Thursday, February 12
• Session 3: Network Analysis with Gephi – Thursday, February 19

All sessions are from 10:00 – 11:00 AM in Library Classroom 103. Registration is required. Participants may bring their own laptop or use one provided by the library.

More information about each session is available below.

Session 1: Basics of Text Analysis with Voyant

• Text Analysis is a set of computational techniques drawn from Natural Language Processing (NLP) that focus on identifying, extracting, organizing, and analyzing patterns within large bodies of textual data. Rather than concentrating on emotional tone alone, text analysis examines what is being discussed, how language is used, and how themes, concepts, and relationships emerge across texts.
• The goal of text analysis is to uncover meaningful patterns and structures in text—such as key themes, word frequencies, topics, trends over time, stylistic features, and relationships between terms—to support interpretation, comparison, and research questions that would be difficult to address through close reading alone.
• In this session, participants will learn how to conduct text analysis using Voyant, a free, open source, web-based text analysis platform.

Session 2: Sentiment Analysis with Digital Scholar Lab

• Sentiment analysis is a Natural Language Processing NLP technique that deals with identifying, extracting, classifying, and quantifying subjective information — such as opinions, attitudes, emotions, or evaluations — expressed in textual data.
• The goal of sentiment analysis is to determine the sentiment orientation (e.g. positive, negative, neutral) and intensity (e.g., very positive, very negative) of texts.
• In this session, participants will learn how to conduct sentiment analysis using Digital Scholar Lab, a cloud-based research platform (access provided by UM Libraries).

Session 3: Network Analysis with Gephi 

• Network analysis is the exploration and visual representation of the connections among entities in a dataset.
• Network visualizations often focus on social networks among people, which are typically represented as “nodes” and their connections, which are typically represented as “edges.”
• The three most common network types are:
  • Co-Occurrence Networks
    • Nodes that appear in the same context
  • Citation/Influence Networks
    • How scholars reference or influence each other
  • Correspondence Networks
    • Record of who communicates with whom, how often, and when
• In this session, participants will learn to conduct network analysis using Gephi, a free, open-source desktop-based network analysis platform.

All events are free and open to the public.

No experience needed.