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Opinion Mapping Travelblogs. Efthymios Drymonas Alexandros Efentakis Dieter Pfoser Research Center Athena Institute for the Management of Information Systems Athens, Greece http:// www.imis.athena-innovation.gr. Introduction. Users create vast amounts of “geospatial” narratives

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opinion mapping travelblogs

Opinion Mapping Travelblogs



Dieter Pfoser

Research Center Athena

Institute for the Management of Information Systems

Athens, Greece



Users create vast amounts of “geospatial” narratives

…travel diaries, travel blogs…

How to quickly assess them?

  • Simple assessment of user-generated geospatial content
  • Visualization
  • Geospatial opinion maps
opinion mapping generating steps
Opinion Mapping generating steps
  • Relating text to location – Geocoding
  • Relating user sentiment to text – Opinion Coding
  • Relating opinions to location – Opinion Mapping
1 relating text to location geocoding
1. Relating text to location – Geocoding
  • Web crawling
  • Geoparsing
  • Geocoding
1 a web crawling
1a. Web Crawling
  • Crawled for travel blog articles
  • Parsed ~ 150k HTML documents
1 b geoparsing processing pipeline overview
1b. Geoparsing -Processing Pipeline Overview
  • GATE
  • Cafetiere IE system
    • Placemaker
    • Placefinder
1 b linguistic preprocessing
1b. Linguistic Preprocessing
  • Tokeniser & Orthographic Analyser
  • Sentence Splitter
  • POS Tagger
  • Morphological Analysis, WordNet
    • Ex. “went south”, “goes south” = “go south”
1 b semantic analysis i ontology lookup
1b. Semantic Analysis: i. Ontology Lookup

Ontology access to retrieve potential semantic class information

1 b semantic analysis ii feature extraction ie engine
1b. Semantic Analysis: ii. Feature Extraction (IE engine)
  • Compilation of semantic analysis rules
  • IE engine uses all previous info
    • Linguistic information (POS tags, orthographic info etc.)
    • Semantic and context information
  • Extraction of spatial objects
1 c postprocessor geocoding
1c. PostProcessor - Geocoding
  • Collecting semantic analysis results and annotating them to the original text
  • Preparing the input to the geocoder module
1 c geocoding
1c. Geocoding
  • Place name info from semantic analysis transformed to coordinates
  • YAHOO! Placemaker for disambiguation
  • YAHOO! Placefindergeocoder
output xml file
output XML file
  • From plain text to structured information
  • Also global document info extracted
2 relating user sentiment to text opinion coding 1 2
2. Relating user sentiment to text– Opinion Coding 1/2
  • OpinionFinder tool
  • Annotates text with positive or negative sentiments
  • Retain paragraphs only containing spatial info
  • Total positive and negative sentiments for each paragraph
3 mapping opinions to location opinion mapping
3. Mapping opinions to location -Opinion Mapping

Scoring method

Spatial grid

Aggregation method

opinion mapping scoring
Opinion Mapping (Scoring)
  • Each paragraph is characterized by a MBR
    • Visualized paragraph’s MBR do not exceed 0.5º x 0.5º
  • Each paragraph’s MBR is mapped to a sentiment color according to users’ opinions
opinion mapping issues
Opinion Mapping (Issues)


  • Multiple paragraphs may partially target the same area (overlapping areas)
  • How to visualize partially overlapping MBRs of different paragraphs and sentiments
opinion mapping spatial grid
Opinion Mapping (Spatial grid)


  • We split earth into small tiles of 0.0045º x 0.0045º (~500m x 500m)
  • Each paragraph’s MBR consists of several such small tiles
opinion mapping aggregation method 1 2
Opinion Mapping (Aggregation Method) 1/2
  • Partially overlapping paragraph MBRs translated to a set of overlapping tiles
    • Sentiment aggregation per tile (for drawing purposes)
      • Instead of sentiment aggregation per MBR
opinion mapping aggregation method 2 2
Opinion Mapping (Aggregation Method) 2/2

An example:

  • For one cell/tile there are four scores:

-1, -2, 1, 0

  • Resulting score is their sum: -2
opinion mapping examples
Opinion Mapping examples

Original MBRs of paragraphs

opinion mapping examples1
Opinion Mapping examples

Paragraph MBRs divided in tiles – Aggregation per tile

  • Aggregating opinions is important for utilizing and assessing user-generated content
  • Total of more than 150k web pages/articles were processed
  • Sentiment information from various articles is aggregated and visualized
  • Relate portions of texts to locations
  • Geospatial opinion-map based on user-contributed information
future work
Future Work
  • Better approach on sentiment analysis
  • More in-depth analysis of the results
  • Examine micro blogging content streams
  • Live updated sentiment information