Thesis oral defense 2015 elvis saravia

Inferring User Interests from Microblog Data through Opinion Mining

Student: Elvis Saravia Advisor: Prof. Yi-Shin Chen

Institution: National Tsing Hua UniversityProgram: International Master Program in Information Systems and Applications (IMPISA)

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Our Journey...￫ Introduction￫ Related Work￫ Objectives￫ Framework￫ Experiment & Results￫ Conclusion & Future Work￫ Q & A

2Inferring User Interests from Microblog Data through Opinion Mining

Introduction￫ Rapid Growth of the Web

○ Web 2.0 (user-generated content)○ Data generated rapidly○ Social Sharing platforms (Facebook & Twitter)

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￫ Online User-Behaviour Data○ Introduced research opportunities ○ The most valuable asset that a company possesses


Online User Behaviors


Interests Emotions

Objectives


￫ This work aims to develop a behavior-based user interests identification model.

￫ The algorithms proposed combine both contextual and emotion analysis to obtain better performance on user interests extraction.

Motivation￫ Economic value

○ Recommendation services (dating sites & ads. targeting)○ Personalized systems (E-commerce & Search engines)

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￫ Personalization○ We love to be uniquely identified ○ Reduce extraction of ambiguous interests


“I may be exactly the same demographic as my neighbor, but that has nothing to do with what I eat.” - Lesperance VP of Digital Marketing and CRM for GrubHub

Related Work￫ Ontology [Mylonas et al. 2008] [Bakalov et al. 2009]

○ Search logs and contextual information to build ontology

￫ Social Structure [Bao et al., WWW 2010] [Wen et al., SIGKDD 2010]○ Focuses on the user social graph (friends and follows)


Related Work ￫ Contextual Information [Piao et al., 2011] [Yang et al., JCIS 2012]

○ Natural Language Processing (NLP) and latent Dirichlet allocation (LDA)

￫ Behavior-Based [Zhou et al. 2008; Xing et al., WWW 2010]○ Collaborative filtering and Social Actions○ User Interactions (printing, copying and saving)


Interest Definition￫ Considerations:

○ Not everything we say or write interests us○ Our interests shouldn’t be ambiguous○ Ranking interests is challenging

￫ Observations:○ Interests ← Motivation ← Positive Emotions [Silvia et al., 2002]○ Our personal Interests are interlinked with our positive emotions

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I am in New York.

I cannot wait for the Facebook Developer Conference


Framework


Contextual analysis + Emotion analysis

Rule-BasedExtraction

Emotion Classification

KeywordExtraction

Pre-processing

Interest Candidates Extraction

Interest Identification

Emotion Tagging & Filtering

Emotion Analysis


Twitter Corpus


Output file

POSTagging

Pre-processing


Pre-processingFilter out information that doesn’t

provide any knowledge or value to user interest identification

Pre-processing￫ Filter out non-English posts and Re-Tweets

￫ Remove useless punctuation marks


I am loving Jeremy Lin! I am loving Jeremy Lin!

For every post (P) in a collection of Tweets (T)

Pre-processing ￫ Remove tweets containing hyperlinks (no emotion)

￫ Remove repeated tweets (same emotion)


Linsanity comes to LA. http://espn.com

.

.Linsanity comes to LA. http://espn.com


Pre-processing


￫ Remove terms less than 3 characters long and terms containing “@” symbol○ (e.g. to and @jason)

@jason I love to go to New York @jason I love to go to New York





KeywordExtraction

Pre-processing




Emotion Analysis


Twitter Corpus

Output file

POSTagging

Pre-processing


Interest Candidates Extraction3-phase interest candidates algorithm to extract as much interest candidates

as possible

Interest Candidates Extraction (1)￫ POS-tagging

○ Part-of-speech tagging○ Nouns, Proper Nouns and Named entities○ Limitation: Naïve interest candidates

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Interest Candidates Extraction (2)￫ Keyword Extraction (RAKE) [Rose et. al 2009]

○ Extract keywords from posts○ Limitation: phrase boundaries

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I enjoyed watching Mr. Bean


Interest Candidates Extraction (3)￫ Previous Phases: Unreliable and Inconsistent

￫ Emerging Interest Concepts? ○ Previous phases cannot extract them○ Provide better insights about users current interests


Wimbledon 2015

Interest Candidates Extraction (3)￫ Rule-Based Concept Extraction [Hsu et al., 2015]

○ Extract frequent emerging concepts based on “wisdom of the crowd”○ 80,000,000 tweets (3,000,000 users)○ 6 patterns were defined


Interest Candidates Extraction (3)




I am loving Wimbledon 2015 #WC2015 Wimbledon 2015

Crowd-wisdom



￫ Combine the results of the 3-phase interest candidates extraction algorithm.○ Repetitive interest candidates were removed





KeywordExtraction

Pre-processing




Emotion Analysis


Twitter Corpus

Output file

POSTagging

Pre-processing


Emotion AnalysisTagging interest candidates with their pertaining emotion

Emotion Classification￫ Pattern based approach

○ Appropriate for grammar informality of tweets

○ Effective for multilingual applications○ Contribution Degree

￫ Why Positive emotions?○ Anticipation, Joy and Trust○ Highly related to motivation and interests.


[Argueta et al., 2015]

Anticipation

Joy

Trust

Surprise

Sadness

Disgust

Anger

Fear

Emotion Analysis￫ Tag Interests with emotion

○ Every interest candidate is tagged with its pertaining emotion○ Original post is classified (no pre-processing)

￫ Only positive emotions considered:○ Anticipation, Joy and Trust○ Negative emotions were not considered in this work


Joy

Anticipation

Trust

Emotion Filtering￫ Filtering process

○ Posts bearing no emotion○ Shorts posts○ Posts that bear opposite emotions (ambiguous)


Joy

Anticipation

Trust


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I am loving Jeremy Lin right now

The traffic today is okay!

.

.

Feeling excited for the ASONAM Conference. #feelingblessed

joy

trust ASONAM Conference

Jeremy Lin


For every post P in a collection of Tweets T




KeywordExtraction

Pre-processing




Emotion Analysis


Twitter Corpus

Output file

POSTagging

Pre-processing


Interest IdentificationRanking interest candidates in each emotion set



￫ Repetitive Interest Candidates○ Interest candidates found under several emotions are kept

￫ Ambiguity○ Remove interests that are ambiguous○ Emotion classifier aids at this very well



￫ Occurrence○ Calculate frequency for each interest candidate (ws) ○ Frequency (f) is based on occurrence

Anticipation:

ws1 (f)ws2 (f)wsn (f)

...

Joy:

Jeremy Lin (f)ws2 (f)wsn (f)

...

Trust:

ACM Conference (f)ws2 (f)wsn (f)

...



￫ Ranking○ Calculate weight for each interest candidate (ws)○ Rank them by weight (w)

Anticipation:

ws1 (w)ws2 (w)wsn (w)

...

Joy:

Jeremy Lin (w)ws2 (w)wsn (w)

...

Trust:

ACM Conference (w)ws2 (w)wsn (w)

...


Experiments and Results2 different types of experiment were conducted

Experiment (1)


￫ Experimental Setup○ 3 active Twitter users (A,B,C)○ The latest 3000+ English posts crawled from feed○ The top-15 most frequent interests per emotion○ Results rated by the users

Evaluation

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0 = not-related1~4 = related5~10 = highly related

User A: Top 15 frequent interests per emotion


Evaluation

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User B: Top 15 frequent interests per emotion


Evaluation

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User C: Top 15 frequent interests per emotion


Evaluation

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User C


User BUser A

Experiment (2)


￫ Experimental Setup○ Online Surveys○ 7 Users (A,B,C,D,E,F)○ Top 5 interests (including 5 sub-category interests)○ The latest 3000+ English posts crawled from feed○ Interests are categorized (ConceptNet)

Categorizing Interests


￫ Hierarchical Interests Extraction○ Top 15 interests in the 3 emotion sets are combined and categorized○ ConceptNet API○ 2 level “is-a” relationship○ Observation: top interest candidates were highly related

Evaluation


Precision of system on raw data (Twitter feed)

Evaluation


● Precision of system when including ambiguous tweets

● Ambiguous tweets bearopposite or no emotion

Evaluation


● Precision of the full system when considering positive emotions

● Average precision of approx. 81% as top performance (top-10).

Evaluation


Performance of componentsper user

Evaluation


Precision comparison of all components evaluated

Conclusion


￫ Positive emotions contribute tremendously to user interests identification as seen in the experiments section.

￫ Emotion Analysis is an important component for the effective ranking of user’s interests and the removal of ambiguous information.

Future Work


￫ Analyze emotion distribution to observe if there are patterns in the change of interests.

￫ Adopt machine learning techniques to automate feature extraction for interest identification.

￫ Improve approach by considering temporal information and negative emotions as a weighting factor.

￫ Improve Interest categorization.

Thanks for listening...


Q & A

Thesis oral defense 2015 elvis saravia

Data & Analytics

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