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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

EfthymiosDrymonas

AlexandrosEfentakis

Dieter Pfoser

Research Center Athena

Institute for the Management of Information Systems

Athens, Greece

http://www.imis.athena-innovation.gr

introduction
Introduction

Users create vast amounts of “geospatial” narratives

…travel diaries, travel blogs…

How to quickly assess them?

motivation
Motivation
  • 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
  • YAHOO! API
    • 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)

Problem:

  • 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)

Solution:

  • 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

conclusions
Conclusions
  • 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
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