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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


2 relating user sentiment to text opinion coding 2 2
2. Relating user sentiment to text– Opinion Coding 2/2

  • Score for this paragraph : +2


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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