DATA VISUALIZATION

By

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S.V.V.D.Jagadeesh

Sr. Assistant Professor

Dept of Artificial Intelligence & Data Science

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LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING

• Session Outcomes
• Text Data
• Levels of Text Representation
• Vector Space Model
• Computing Weights
• Zipf’s Law
• Tasks Using Vector Space Model

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S.V.V.D.Jagadeesh

Friday, October 10, 2025

Previously Discussed Topics

LBRCE

DATA VISUALIZATION

At the end of this session, Student will be able to:

• Understand different text visualization techniques(Understand-L2)

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Session Outcomes

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Single Document Visualizations

• TagClouds
• WordTrees
• TextArc
• Arc Diagrams
• Literature Fingerprinting

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Tag Clouds

• Tag clouds, also known as text clouds or word clouds, are layouts of raw tokens, colored and sized by their frequency within a single document.
• Text clouds and their variations, such as a Wordle are examples of visualizations that use only term frequency vectors and some layout algorithm to create the visualization

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Word Trees

• The WordTree visualization is a visual representation of both term frequencies, as well as their context.
• Size is used to represent the term or phrase frequency.
• The root of the tree is a user-specified word or phrase of interest, and the branches represent the various contexts in which the word or phrase is used in the document.

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Text Arc

LBRCE

DATA VISUALIZATION

• We can extend the representation of word distribution by displaying con nectivity.
• There are several ways in which connections can be computed.
• TextArc is a visual representation of how terms relate to the lines of text in which they appear.
• Every word of the text is drawn in order around an ellipse as small lines with a slight offset at its start.
• As in a text cloud, more frequently occurring words are drawn larger and brighter, Words with higher frequencies are drawn within the ellipse, pulled by its oc currences on the circle.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Text Arc

LBRCE

DATA VISUALIZATION

• Arc diagrams are a visualization focused on displaying repetition in text or any sequence.
• Repeated subsequences are identified and connected by semicircular arcs.
• The thickness of the arcs represents the length of the subsequence, and the height of the arcs represents the distance between the subsequences.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Arc Diagrams

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Literature Fingerprinting

LBRCE

DATA VISUALIZATION

• Literature fingerprinting is a method of visualizing features used to characterize text.
• Instead of calculating just one feature value or vector for the whole text (this is what is usually done), we calculate a sequence of feature values per text and present them to the user as a characteristic fingerprint of the document.
• This allows the user to “look inside” the document and analyze the development of the values across the text.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Literature FingerPrinting

LBRCE

DATA VISUALIZATION

• In most cases of document collection visualizations, the goal is to place similar documents close to each other and dissimilar ones far apart.
• This is a minimax problem and typically O(n^2).
• We compute the similarity between all pairs of documents and determine a layout.
• The common approaches include:
• Self-Organizing Maps
• Themescapes
• Document Cards

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Document Collection Visualizations

LBRCE

DATA VISUALIZATION

• A self-organizing map is an unsupervised learning algorithm using a collection of typically 2D nodes, where documents will be located.
• Each node has an associated vector of the same dimensionality as the input vectors (the document vectors) used to train the map.
• We initialize the SOM nodes, typically with random weights.
• We choose a random vector from the input vectors and calculate its distance from each node.
• We adjust the weights of the closest nodes (within a particular radius), making each closer to the input vector, with the higher weights corresponding to the closest selected node.
• As we iterate through the input vectors, the radius gets smaller.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Self Organizing Maps

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Self Organizing Maps

LBRCE

DATA VISUALIZATION

• Themescapes are summaries of corpora using abstract 3D landscapes in which height and color are used to represent density of similar documents.
• The taller mountains represent frequent themes in the document corpus (height is proportional to number of documents relating to the theme).

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S.V.V.D.Jagadeesh

Friday, October 10, 2025

Themescapes

LBRCE

DATA VISUALIZATION

• Document cards are a compact visualization that represents the document’s key semantics as a mixture of images and important key terms, similar to cards in a top trumps game.
• The key terms are extracted using an advanced text mining approach based on an automatic extraction of document structure.
• The images and their captions are extracted using a graphical heuristic, and the captions are used for a semi semantic image weighting.
• Furthermore, the image color histogram is used to classify images into classes (class 1: photography/rendered image, class 2: diagram/sketch/graph, class 3: table) and show at least one representative from each non-empty class

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Document Card’s

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Document Card’s

LBRCE

DATA VISUALIZATION

• Here we investigate several text visualization techniques that involve meta data or otherwise go beyond the typical term-vector-based visualizations.
• Common techniques include
• Software Visualization
• Search Result Visualization
• Temporal Document Collection Visualizations
• Representing Relationships

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Extended Text Visualizations

LBRCE

DATA VISUALIZATION

• Eick et al. developed a visualization tool called SeeSoft that visualizes statistics for each line of code (i.e., age and number of modifications, programmer, dates).
• Each column represents a source code file with the height representing the size of the file.
• If the file is longer than the screen, it continues into the next column.
• In the classic SeeSoft representation, each row represents one line of code.
• Since the number of lines is too large for one row, each line of code is represented by a pixel in the row.
• This increases the number of lines able to be displayed.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Software Visualizations

LBRCE

DATA VISUALIZATION

• Color is used to represent the call count.
• The more red a line is the more often the line is called, and thus is a key hot-spot.
• A blue line is an infrequently called one.
• Color can be used to represent other parameters, such as time of last modification or number of modifications.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Software Visualizations

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Software Visualizations

LBRCE

DATA VISUALIZATION

• Marti Hearst developed a simple query result visualization foundationally similar to Keim’s pixel called TileBars, which displays a number of term-related statistics, including frequency and distribution of terms, length of document, term-based ranking, and strength of ranking.
• Each document of the result set is represented by a rectangle, where width indicates relative length of the document and stacked squares correspond to text segments.
• Each row of the stack represents a set of query terms, and the darkness of the square indicates the frequency of terms among the corresponding terms.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Search Result Visualizations

LBRCE

DATA VISUALIZATION

• Titles and the first words from the document appear next to its TileBar.
• Each large rectangle indicates a document, and each square within the document represents a text segment.
• The darker the tile, the more frequent is the query term set.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Search Result Visualizations

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Search Result Visualizations

LBRCE

DATA VISUALIZATION

• ThemeRiver, also called a stream graph, is a visualization of thematic changes in a document collection over time.
• This visualization assumes that the input data progresses over time.
• Themes are visually represented as colored horizontal bands whose vertical thickness at a given horizontal location represents their frequency at a particular point in time.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Temporal Document Collection

LBRCE

DATA VISUALIZATION

• Jigsaw is a tool for visualizing and exploring text corpora.
• Jigsaw’s calendar view positions document objects on a calendar based on date entities identified within the text.
• When the user highlights a document, the entities that occur within that document are displayed.

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S.V.V.D.Jagadeesh

Friday, October 10, 2025

Temporal Document Collection

LBRCE

DATA VISUALIZATION

• Wanner et al. developed a visual analytics tool for conducting semi automatic sentiment analysis of large news feeds.
• While the tool automatically retrieves and analyzes RSS feeds with respect to positive and negative opinion words, the more demanding news analysis of finding trends, spotting peculiarities, and putting events into context is left to the human expert.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Temporal Document Collection

LBRCE

DATA VISUALIZATION

• Jigsaw also includes an entity graph view in which the user can navigate a graph of related entities and documents.
• In Jigsaw, entities are connected to the documents in which they appear.
• The Jigsaw graph view does not show the entire document collection, but it allows the user to incrementally expand the graph by selecting documents and entities of interest.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Representing Relationships

LBRCE

DATA VISUALIZATION

• The Jigsaw list view is an alternative to the graph view in that it allows the user to explore relationships between various entity types and documents.

S.V.V.D.Jagadeesh

Friday, October 10, 2025

Representing Relationships

LBRCE

DATA VISUALIZATION

• Session Outcomes
• Single Document Visualizations
• Document Collection Visualizations
• Search Result Visualizations

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S.V.V.D.Jagadeesh

Friday, October 10, 2025

Summary

LBRCE

DATA VISUALIZATION