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VISA: A VIsual Sentiment Analysis System
Sept. 2012
Dongxu Duan1 Weihong Qian1 Shimei Pan2
Lei Shi3 Chuang Lin4
1 IBM Research — China
2 IBM T. J. WatsonResearch Center
3 Institute of SoftwareChinese Academy of Sciences
4 Tsinghua University
2
What is Sentiment Analysis• Sentiment analysis or opinion mining refers to the application
of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials. ---- From Wikipedia
• A survey of sentiment analysis works by Pang and Lee in 2008:“Opinion mining and sentiment analysis”, cited 1189 times in Google Scholar, including 326 references
A probably earliest study:
3
MotivationThe truth: sentiment analysis is becoming even more important– Corporate
* Brand analysis, sales campaign design, etc. * Crisis relationship management
– Government• As we all know ..
Observations:– Sentiment analysis technologies are going deeper and versatile:
* Aspect-oriented, domain-specific lexicon expansion, MT technology
– The average users are still leveraging rather simple sentiment results
• It’s hard for them (even domain expert) to understand sophisticated SA results
– There is big gap and huge potential for sentiment visualization (visual opinion mining)
4
Agenda
• Related Works
• Research Problem and Challenges
• Sentiment-Tuple based Data Model
• VISA System Framework
• Visualization Optimizations
• Cases
• User Studies
• Summary
Basic Sentiment Representation• Raw text/table or simple visualization
Brand Association Map
COBRA (COrporate Brand and Reputation Analysis)
Behal et al. (HCI 2009)
Opinion Observer
Liu et al. (KDD 2005); Liu et al. (IW3C2 2005)
Visual Sentiment Analysis of RSS News Feeds
Wanner et al. (VISSW 2009)
Pulse: Mining Customer Opinions from Free Text
Gamon et al. (IDA 2005)
Visualizing Sentiments in Financial Texts
Ahmad and Almas (IV2005)
Visual Analysis of Conflicting Opinions
Chen et al. (VAST 2006)
Who Votes For What? A Visual Query Language for Opinion Data
Draper and Riesenfeld (Vis 2008)
Visual Opinion Analysis of Customer Feedback Data
Summary Report of printers
Scatterplot of customer reviews on printers
Circular Correlation Map
Oelke et al. (VAST 2009)
OpinionSeer: Interactive Visualization of Hotel Customer Feedback
Wu et al. (InfoVis 2010)
Taking the Pulse of the Web: Assessing Sentiment on Topics in Online Media
Brew et al. (WebSci 2010)
Understanding Text Corpora with Multiple Facets
Shi et al. (VAST 2010)
18
Research Problem• Can we design a sentiment visualization system that:
– Show how the sentiment evolves over time (trend)– Visualize both the sentiment analysis results and the structured
facet data, e.g. profile of the reviewer (facet)– Rather than only showing which document or feature tends to be
positive or negative, also demonstrate how the positives/ negatives are described in documents (context)
• Most existing sentiment visualization fails to meet all the requirements simultaneously
– Our VISA design is based on the TIARA prototype, which already brings together most features (trend, context, facet switching)
19
Retrospect on TIARA Visualization(Emergency Room Record)
20
Challenges for TIARA Sentiment Visualization• Failure of the document trend visualization
– Binary/ternary/scored classification of document-level sentiments will drop valuable pieces
BUT: It has BED BUGS and they BITE me!!!
21
Challenges for TIARA Sentiment Visualization• Keyword Summarization
– Content visualized are keywords summarized from all the text, not echoing the sentiment-centric design
• Structured Facet– Sentiment-aware facet associations and distributions– Spatial (location) information
• Comparison– Categorical, temporal comparison, and sentiment comparison
as well
• Compatibility with sentiment analysis engines– Consumability of all kinds of sentiment analysis results
Sentiment Tuple• {Aspect, feature, opinion, polarity}
– Aspect: a sub-topic shared by some document In a hotel review, the room, the view, or the service– Feature: specific object the users are commenting
Entity, person, location, or abstract concepts– An opinion is a particular word or phrase describing a feature– Polarity of the opinion word/phrase in the context
……
Sentiment Analysis Model
aspect: feature: opinion: polarityaspect: feature: opinion: polarity……
aspect: feature: opinion: polarityaspect: feature: opinion: polarity……
aspect: feature: opinion: polarityaspect: feature: opinion: polarity……
{ “view”, + }
Aggregate
Keyword Summarization (TIARA)
A set of topics {T1, …Ti,… TN }
A set of keywords
{W1, …, Wj, …, WM}
A set of topic probabilities
{…, P(Ti | Dk), …}
A set of word probabilities
{…, P(Wj | Ti), …}
kth document in the collection
Rank the topics to present most valuable ones first
Select keyword sub-set for each time segment for content summary
{…} t-1, {…, Wj, …}t, {…} t+1,
VISA Sentiment Keyword Summarization
{C1, …Ci,… CN }
A set of sentiment keywords(opinions/features)
{W1, …, Wj, …, WM}
A set of topic probabilities
{…, P(Ti | Dk), …}
A set of word probabilities
{…, P(Wj | Ti), …}
kth document in the collection
Let user select to compare aspects of a hotel or an aspect of several hotels
Select keyword sub-set for each time segment for sentiment summary
{…} t-1, {…, Wj, …}t, {…} t+1,
Aspects/Hotels
VISA Mashup Visualization
SentimentTuple TrendSentiment
Tuple Trend
FacetCorrelations
FacetCorrelations
SentimentSnippets
SentimentSnippets
SearchSearch
Sentiment-CentricDocumentRanking
Sentiment-CentricDocumentRanking
FiltersFilters
26
VISA Sentiment Visualization Framework
• Offline: – Document pre-processing– Sentiment analysis– Meta data parsing– Indexing
• Online: – Data Retrieval– Visualization– Interactions
Offline Analysis
Raw DataRaw Data Reader
Extractor
StatisticManager
DictionaryDictionary
IndexWriterIndexIndex
Meta Data Sentiment Data
Segment ExtractorSegment Extractor
Sentence ExtractorSentence Extractor
Text ExtractorText Extractor
Entity PolicyEntity Policy
Filter OpenNLP
Sentiment Entity Class No/Not
aspect: feature: opinion: polarityaspect: feature: opinion: polarity
Data Analysis Framework
Offline Analysis
Raw DataRaw Data Reader
3rd Party Sentiment Analysis Framework
IndexWriterIndexIndex
Meta Data Sentiment Dataaspect: feature: opinion: polarityaspect: feature: opinion: polarity
Data Server
Query ParserQuery Parser
Data RetrievalData Retrieval
Lucene
Hermes
Index
HttpServletHttpServlet
VISA
Data AdapterData Adapter
Sentiment Trend Optimizations• Sentiment tuple based negative/positive/(neutral) trends
Positive
Negative
Y axis: sentiment valueY axis: sentiment value
X axis: timeX axis: time
Time Sensitive Feature/Opinion wordsTime Sensitive Feature/Opinion words
Sentiment-Centric Interactions
32
Case Study ---- Summarizing Hotel Reviews
• Initial View
33
Case Study ---- Summarizing Hotel Reviews
• Switch to ”Family” type only
(traveling in this type)
34
Case Study ---- Summarizing Hotel Reviews
• Click on the “Free” sentiment word
(want to enjoy the free time or free breakfast?)
• It’s 30 min distance from the harbor!
35
Case Study ---- Summarizing Hotel Reviews
• For two selected hotels
• Drill down to the “cleanliness” and “room” aspects
• Switch to the negative sentiments
36
Case Study ---- Summarizing Hotel Reviews
• Comparing the recent reviews
37
Case Study ---- NFL on Twitter
• Crawling tweets from Twitter on the topic of National Football League (NFL), from 03/2011 to 08/2011. (when the famous lock out happened)
• 665360 tweets from 307973 users, with an average length of 16.8 words.
• Tweet collection pre-processing:– Classify into 5 content topics: “season play”, “player draft”,
“lockout bad”, “lockout end” and “football return”.– Categorize according to the subject of the sentiments – 32 NFL
teams, by manually creating relevant subject keyword list for each team (full/nick name, city, stadium, head, owner and super stars)
38
Case Study ---- NFL on Twitter• Overview of sentiments on content topics
– Reach peak in July when the new CBA signed
39
Case Study ---- NFL on Twitter
• Subject-comparing view on 4 NFL Teams– “Green Bay Packers”, “Pittsburgh Steelers”, “New York Jets”, “New England Patriots”– A very large RED “CBA” for the Steelers: the only team to vote “NO” to CBA– “Brett Favre” for the Packers: the former NFL all-star quarterback in Packers, who has
claimed to return for several times. The fans are tired of the similar news at all.
40
User Study ---- Setup• Subject
– VISA System with all functionalities– TripAdvisor.com– A plain text editor with search function
• Data– HK hotel cases with 3 hotels’ reviews– Both structured (ratings) and unstructured (review
comments) data inputs
• User– 12 users (7 male, 5 female), age 26~35– Each is given a gift as incentive
• Task– TI: look up specific sentiment-related information of a hotel
(e.g. traveler’s ratings).– T2: summarize opinions on a general aspect of a hotel (e.g.
the view of a hotel)
• Procedure– Within-subject design: user perform all tasks with all the
systems– Record user demographics, time of completion and
satisfactions and open-ended questions
TripAdvisorTripAdvisor
Text EditorText Editor
VISAVISA
41
User Study ---- Objective Results
• Three metrics: Elapsed time (in minutes), task completion rate and task correctness.
0
0.5
1
1.5
2
2.5
3
VISA
TripAdvisor
TextEditor
VISA 1.66 1 0.75
TripAdvisor 2.94 0.81 0.42
TextEditor 2.69 0.86 0.67
Time(min) Completion Correctness
Significant advantages of VISA over the compared systems(t-test significance p< 0.004~ 0.034)
42
User Study ---- Subjective Results
• Three metrics: Usefulness, userability and satisfaction.
0
1
2
3
4
5
VISA
TripAdvisor
TextEditor
VISA 4.58 4.08 4.29
TripAdvisor 2.46 2.67 2.38
TextEditor 2.5 2.33 2.17
Usefulness Usability Satisfaction
Subjective Evaluation Results
43
User Study ---- Open Surveys
• Why VISA is thought better than the baseline systems:– “mash-up visualizations” and “rich interactions”– “Mash-up visualizations provide more information and it’s
quite intuitive”, “rich interactions make it easy to search what I want to know”
– Improvements to VISA: “it now needs some learning efforts to use VISA”, “It could introduce better UI design and richer interactions”.
44
Summary
• We have presented the VISA system for generic sentiment visualization purpose– The backend core is the new sentiment-tuple definition, as well
as the faceted data model– In visualization, we introduce several critical optimizations over
TIARA in sentiment visualization scenarios: sentiment-tuple based trending, sentiment keywords, comparison, sentiment in document context, interactions
– Evaluated with two real-life case studies– Conduct formal user study to compare with two baseline
systems and demonstrate the clear advantage
45
Thank You
MerciGrazie
Gracias
Obrigado
Danke
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