Visual Text Analysis

Domain: Reviews / (Medical) Reports

Rose Plots

User-directed Sentiment Analysis: Visualizing the Affective Content of Documents

Data used?

  • for case Study
    • Amazon reviews (  5 diffrent products )
  • Documents, sentences / phrase
  • Diffrent formats ( .doc, .xml, ... )
  • designed for a varietz of genres ( IN-SPIRE  Approach)

Analysis Technique

  • Lexical Approach
    • compare doc. with a affect lexicon
    • lexicon derived from General Inquirer (GI)
    • semi-supervised bootstrapping (expand dictionaries)

Visualization Technique

  • Multiple Views (inspired by IN-SPIRE)
  • Galaxy View
  • Two diffrent types of Rose Plots
    • large petals encode high affect score
    • included box plot into petal
    • petals are group (two affect categories)
    • unit circle shows deviation of expected value
  • Histogram beneath each Ros Plot (number of doc.)
  • Correlation Tool ( show distribution of products)

Problems

  • no good overview visualization
  • no aid to maintain mental map
  • not scalable

UTOPIAN

User-Driven Topic Modeling based on Interactive NMF

Data used?

  •  Case Study
    • academic papers (InfoVis VAST)
    • Product reviews ( Tv show, Cars)
    • newsgroup data ( 20News, well clustered)
  • Input for t-SNE
    • High Dimensional Vectors

Application Pipeline

SS-NMF

t-SNE

Visualization/
Rendering

  • two diffrent threads for t-SNE & SS-NMF
  • allows real-time interaction of visualization

Analysis Technique

  • Nonnegative Matrix Factorization (NMF)
    • advantages
      • consistency over multilpe runs
      • emipirical convergence
  • t-SNE (modified)
    • reveal implicit groupings

Visualization Technique

  • node-link diagram
  • Continous visualization
    • real time interaction with Algorithm Output
    • simulation like behavior

Opinion Seer

Interactive Visualisation of Hotel Customer Feedback

Data Used?

  • hotel  cutomer review data ( TripAdvisor )
    • Hotel Profile
    • Customer Profile
    • Review data

Analysis Technique

  • Feature Based Opinion Mining
    • Free Text vs. Ratings
  • New Concept of Uncertainty
    • reviews have additional value to positive / negative
  • Opinion Combination
    • based on Subjective Logic

Visualization Technique

  • Multiple Visualizations
    • Opinion Wheel
      • Opinion circle (Radial Visualization)
      • Opinion Triangle (Triangular Scatterplot )
    • Tag clouds
      • organized in a table

FacetAtlas

Multifaceted Visualization for Rich Text Corpora

Data used?

  • Medical Reports (Google Health)
  • Article Structure (Paragraphs containing facets)

Analysis Technique

  • new entity- relational data model introduced
  • data needs to be transformed into model
    • extract multifaced entities (name entity recognition)
    • construct similarity graph (consine / topic-level similarity)
    • create new search indices (lucene)

Visualization Approach

Overall Visuall design

  • visualize global and local patterns
  • integrate search into visualisation (visual query)
  • simply visualize multifacet relationships

Visualization Technique

  • Density Map + Multifacet Graph
    • visual encoding of data model
    • visually simplify data exploration (edge bundling / highlighting)
    • interactions to examine data from diffrent perspectives

Time Density Plots

Feature-Based Visual Sentiment Analysis of Text Document Streams

Data Used?

  • Two diffrent Types of Data
  • missing limited topic coverage
    • Web Surveys (50,000 Surveys)
      • all possible complaints ( purchase, delivery, product )
      • comment on features in detailed way
      • Cutomer Web survey
    • Rss News Items (16,000 items)
      • real-time distribution, repostings
      • no specific feature to indentify
      • explore spread of Opinion and reasons
      • 50 Presidential Election feeds

Analysis Technique

  • no domain-dependant sentiment word list
    • rely on very general sentiment words
  • Linguistic Preprocessing
  • Feature Extraction
  • Sentiment Word Detection
  • Sentiment-to-Feature Mapping (Hybrid approach)
    • manually defined syntactic reference patterns
    • paired with typed-dependency parsers
    • modified distance-based mapping
    • awareness of uncertainty (not precise vs. guide user)

Visualization Technique

  • Pixel Map Calendars - ( scatterplot )
    • display overall data distribution
    • one pixel represents one document
    • pixel ploted in bins - color encodes overall sentiment
    • axes of bin can have different time unit
  • Time Density Plots
    • Sequential Sentiment Track - ( rectangular bars )
      • preserve temporal order
      • whitespace reduction - no overplotting
      • certainty encode in bar height
    • Time Density Track - ( Area Graph )
      • height of curved determinated by distance of two time stamps
      • high curves indicates max. Level of Interesst

Lexichron

Visual Analysis of Conflicting Opinions

Data Used?

  • Amazon Customer Reviews  (3.168 Reviews)
  • Only 4-5 stars ond 1-2 stars ratings
  • about size of Scientific Abstract
  • Diffuse Opinions in diffrent Reviews
    • based on Da Vinci Code book review

Analysis Technique

  • Linguistic Variation  (TermWatch, Term Variation graph)
    • TermWatch - Clustering (CPCL implemented with SLME)
  • Predictive Text Analysis  (Decision Trees - SVM )

Visualization Technique

  • Term Variation Graph
    • display cluster overview & detail
    • time series data ploted in graph
  • unfolded View of one cluster (pink Background)
  • Time series Data ploted in Graph
  • timestamps ploted on edge (year-month)
  • Positive Term alongside Negative Terms
  • monthwise separation
  • Coordinated View - ( multiple Visualizations, Improvise )
    • Lexichron - Arc Diagram
      • top half positive reviews - bottom half negative review
      • arcs connect months with common Terms
      • arc thickness encodes # of common Terms
      • Bar thickness encodes # of Term per Month
    • Graph View
    • multicolumn Tables

Pixel Sentiment Geo Map

Visual sentiment analysis of customer feedback streams using geo-temporal term associations

Data Used?

  • Broad Number of Application
    • Theme park attraction, product surveys, hotel reservations, IT services, movies...
  • Web Surveys
    • Historic Data (after cutomer Purchase)
    • Tested with 52,189 Reviews (96,987 sentences)
  • Tweets
    • Real-time (reaction to Movie)
    • limited to 140 charachters
    • tend to have heavy use of abbriviations
    • open-ended Data Sources (text streams)

Analysis Technique

  • Feature-based Sentiment Analysis (use own algorithm)
  • Term Association with Hypothesis Tests (Likelihood Ratio Test)
    • Focus on geo-spatial information ( geo-based Term Association )
    • Covers almost whole spectrum of frequencies ( Likelihood Ratio Test)

Visualization Technique

  • Geo Maps
    • Pixel Sentiment Geo Map
    • Key Term Geo Maps
  • Pixel-cell Based Sentiment Calendar
    • similar approach as in Pixel Map Calendars
    • row encodes diffrent feature    
    • organized monthly-year
  • Self-organizing Term Association Map ( SOM )
  • Pixel Sentiment Geo Maps
    • overlapping and data density
    • clustering and use of glyphs
    • disapperance of original data
    • Pixel Placment Algorithm  (Bresham - Midpoint)
    • avoid random patterns place by priority (sentiment)
    • radial layout of pixel placement
  • Pixel Placment Algorithm
  • Radial Layout
  • Self-organizing Term Association Map ( SOM )
    • enriched version of Word Cloud with semantic context
    • grouped into associations
    • color encodes sentiment
    • saturation encodes strength of sentiment

Key Term Geo Map

Visual sentiment analysis of customer feedback streams using geo-temporal term associations

Visualization Technique

  • Key Term Geo Maps
    • Key Term Geo Map
    • Key Term Distribution Map
  • Key Term Geo Map
    • diffrent levels of detail
    • map key terms to zip code areas - ( not always possible )
    • change mapping between text & geo hierachy
    • Color, Size encoded accordingly ( respect overlaping )
  • Key Term Distribution Map
    •  separate by sentiment ( negative vs. positive )
    • plot heat map on geo map ( Gaussian bluring func. )
    • overplot two heat maps
    • overlaped results
    • merge colors

Visual Text Analysis

By Giuliano Castiglia

Visual Text Analysis

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