1. 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

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

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

4. 4 Opinion Mapping generating steps 1.Relating text to location – Geocoding 2.Relating user sentiment to text – Opinion Coding 3.Relating opinions to location – Opinion Mapping

5. 1. Relating text to location – Geocoding 5 a)Web crawling b)Geoparsing c)Geocoding

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

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

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

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

10. 1 b. 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 10

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

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

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

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 14

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

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

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 17

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 18

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 19

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 20

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 21

22. Opinion Mapping examples 22 Original MBRs of paragraphs

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

24. Opinion Mapping examples 24 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 25

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

27. End.. Questions? 27