Opinion Mapping Travelblogs

Efthymios Drymonas Alexandros Efentakis

Dieter Pfoser

Research Center AthenaInstitute for the Management of Information Systems

Athens, Greecehttp://www.imis.athena-innovation.gr

Users create vast amounts of

“geospatial” narratives

…travel diaries, travel blogs…

How to quickly assess them?

2

Introduction

• Simple assessment of user-generated

geospatial content

• Visualization

• Geospatial opinion maps

3

Motivation

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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

1. Relating text to location – Geocoding

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a) Web crawling

b) Geoparsing

c) Geocoding

1a. Web Crawling

• Crawled for travel blog articles

• Parsed ~ 150k HTML documents

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1b. Geoparsing -Processing Pipeline Overview

• GATE

• Cafetiere IE

system

• YAHOO! API

– Placemaker

– Placefinder7

1b. Linguistic Preprocessing

• Tokeniser & Orthographic Analyser

• Sentence Splitter

• POS Tagger

• Morphological Analysis, WordNet – Ex. “went south”, “goes south” = “go south”

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1b. Semantic Analysis: i. Ontology Lookup

Ontology access to retrieve potential

semantic class information

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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 objects10

1c. PostProcessor - Geocoding

• Collecting semantic analysis

results and annotating them to

the original text

• Preparing the input to the

geocoder module

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1c. Geocoding

• Place name info from semantic analysis

transformed to coordinates

• YAHOO! Placemaker for disambiguation

• YAHOO! Placefinder geocoder

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output XML file

• From plain text

to structured

information

• Also global

document info

extracted13

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

2. Relating user sentiment to text–

Opinion Coding 2/2

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• Score for this paragraph : +2

3. Mapping opinions to location -Opinion Mapping

Scoring method

Spatial grid

Aggregation method

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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

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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

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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

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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

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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

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Opinion Mapping examples

22Original MBRs of paragraphs

Opinion Mapping examples

23Paragraph MBRs divided in tiles – Aggregation per tile

Opinion Mapping examples

24Final result

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

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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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End.. Questions?

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