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Opinion Mapping Travelblogs

This research discusses a method for assessing vast amounts of user-generated geospatial narratives, particularly travel blogs and diaries. It outlines a multi-step process utilizing geocoding, opinion coding, and visualization techniques to create geospatial opinion maps. The process involves web crawling for data collection, linguistic preprocessing, and sentiment analysis to correlate user opinions with specific locations. By aggregating sentiments from over 150,000 travel-related articles, this approach provides a new way to visualize the emotional landscape of travel narratives, aiding both researchers and travelers.

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Opinion Mapping Travelblogs

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  1. 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

  2. Introduction Users create vast amounts of “geospatial” narratives …travel diaries, travel blogs… How to quickly assess them?

  3. Motivation • Simple assessment of user-generated geospatial content • Visualization • Geospatial opinion maps

  4. Opinion Mapping generating steps • Relating text to location – Geocoding • Relating user sentiment to text – Opinion Coding • Relating opinions to location – Opinion Mapping

  5. 1. Relating text to location – Geocoding • Web crawling • Geoparsing • Geocoding

  6. 1a. Web Crawling • Crawled for travel blog articles • Parsed ~ 150k HTML documents

  7. 1b. Geoparsing -Processing Pipeline Overview • GATE • Cafetiere IE system • YAHOO! API • Placemaker • Placefinder

  8. 1b. Linguistic Preprocessing • Tokeniser & Orthographic Analyser • Sentence Splitter • POS Tagger • Morphological Analysis, WordNet • Ex. “went south”, “goes south” = “go south”

  9. 1b. Semantic Analysis: i. Ontology Lookup Ontology access to retrieve potential semantic class information

  10. 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

  11. 1c. PostProcessor - Geocoding • Collecting semantic analysis results and annotating them to the original text • Preparing the input to the geocoder module

  12. 1c. Geocoding • Place name info from semantic analysis transformed to coordinates • YAHOO! Placemaker for disambiguation • YAHOO! Placefindergeocoder

  13. output XML file • From plain text to structured information • Also global document info extracted

  14. 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

  15. 2. Relating user sentiment to text– Opinion Coding 2/2 • Score for this paragraph : +2

  16. 3. Mapping opinions to location -Opinion Mapping Scoring method Spatial grid Aggregation method

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. Opinion Mapping examples Original MBRs of paragraphs

  23. Opinion Mapping examples Paragraph MBRs divided in tiles – Aggregation per tile

  24. Opinion Mapping examples Final result

  25. 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

  26. Future Work • Better approach on sentiment analysis • More in-depth analysis of the results • Examine micro blogging content streams • Live updated sentiment information

  27. End.. Questions?

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