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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
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
1. Relating text to location – Geocoding
5
a) Web crawling
b) Geoparsing
c) Geocoding
1a. Web Crawling
• Crawled for travel blog articles
• Parsed ~ 150k HTML documents
6
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”
8
1b. Semantic Analysis: i. Ontology Lookup
Ontology access to retrieve potential
semantic class information
9
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
11
1c. Geocoding
• Place name info from semantic analysis
transformed to coordinates
• YAHOO! Placemaker for disambiguation
• YAHOO! Placefinder geocoder
12
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
15
• Score for this paragraph : +2
3. Mapping opinions to location -Opinion Mapping
Scoring method
Spatial grid
Aggregation method
16
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
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
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
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
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
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
25
Future Work
• Better approach on sentiment analysis
• More in-depth analysis of the results
• Examine micro blogging content streams
• Live updated sentiment information
26
End.. Questions?
27