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From Bias to Opinion: a Transfer-Learning Approach to Real-Time Sentiment Analysis

Pedro , Adriano, Wagner, Virgilio Universidade Federal de Minas Gerais, Brazil

4/10/2012 Presentation for Comp722 Data Mining, Kaiwen Qi

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Outline

Background and Paper Purpose Quantify Bias Exploiting Bias for Sentiment Analysis Conclusions

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Social Media and Opinionated Data

From Pedro’s PPT

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Background: Sentimental Analysis

GoalDetermine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document.

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Sentimental Analysis

Example: http://www.tweetsentiments.com/analyze?utf8=%E2%9C%93&q=Lady+Gaga&topic=false&commit=Analyze+Tweets

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Sentimental Analysis

Another name: Opinion Mining

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Use sentimental analysis for:

Help companies keep on top of issues and respond to trends impacting on business.

Gather new customer insights from unstructured-content (gathered from social networks). 

Determine the degree to which a sentiment is positive, negative or neutral for the entire content or a segment of the content.

Identify those voices and publications influencing customers and competitors. Adjust and optimize communication strategies. Use it to direct strategic decisions such as modifying marketing messages,

customer service or product development. Receive early warnings of market developments. Manage and preserve brand equations and reputations. Monitor public opinion Summarize the aggregated sentiment of online society

http://passionjunkie.hubpages.com/hub/Sentimental-Analysis-Business-Insights-that-Help-you-Grow

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Real-Time v.s Traditional Sentiment Analysis

Traditional: Uses static and well-controlled scenarios that target analysis of

reviews of products and services Pre-defined Lists of positive and negative words

Real-Time: Lack of labeled textual data Dynamicity of discussion : dynamic/concept drift/non-stationary

distribution

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Dynamic Discussion and lack of labeled data

From Pedro’s ppt

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Task

What is the time-invariant pattern that does not require significant labeling efforts and supports real-time sentiment analysis?

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Proposal

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Social Media Endorsements as Evidence of User Bias

Endorsements : Interactions among users in which one user implicitly agrees with another.

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Bias and opinions

From Pefro’s PPT

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Proposal intension

How can the sociological definition of bias be implemented into a social media platform by only considering social interactions among users?

How can bias information be converted into information on the sentiment that is associated with the generated content?

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Modeling User Bias Prediction

Determine the most similar users based on individual endorsements

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Measuring bias

We label users whose bias is clearly identifiable as representative of a particular side in a discussion

From Pefro’s PPT

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Modeling User Bias Prediction

Activity similarity The similarity considering the users that both

pair of users retweeted

Passive similarity The similarity considering the users that

retweeted both pair of users

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The Opinion Agreement Graph G=(V,E)

Vertices : User Edge: global judgment of the connected users

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The Opinion Agreement Graph

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From Pefro’s PPT

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Explanation

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Measure Bias

Attractors: sever as reliable sources of bias knowledge

The bias of each node is its proximity from attractors that represent that side to all users in U

Random walk: to measure proximity among nodes

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Bias measurement

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Case Study

Brazilian 2010 Presidential Elections Brazilian 2010 Soccer League

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Bias in Elections Discussions

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Bias in Elections Discussions

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Bias in Soccer Discussions

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Bias in Soccer Discussions

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Bias is a consistent pattern

From Pedro’s PPT

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Consistent Bias

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Background: Transfer Learning

Using learned knowledge from one context to benefit further learning tasks in other contexts

Benefit from knowledge Obtained from similar Tasks or domains

From Liyuan Dai’s paper

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Transfer Learning

Example:

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Transferring bias from user to content

From Pedro’s PPT

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Relationship between terms and users bias

From Pefro’s PPT

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Relationship between terms and users bias

From Pedro’s PPT

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Relationship between terms and users bias

From Pedro’s PPT

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Message Polarity Determination

The term of highest polarity in each tweet:polarity = argmax(p ҄(polarity = x|t))

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Evaluating the Knowledge Transfer Process

F1 accuracy v.s number of user with bias When the bias of 15% of users commenting on politics is known, F1=85%

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Evaluating the Knowledge Transfer Process

F1 v.s number of users with bias When the bias of 15% of users commenting on politics is known, F1=90%

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Comparison to SVM SVM F1 decreases due to the textual feature distribution Bias-based is better, not using labeled textual data Maintain a stable F1, as it incrementally incorporate bias

information on new terms by propagating user bias.

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Comparison to SVM SVM F1 decreases Bias-based = SVM, but not require labeled textual data

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Analyzing a Soccer Math in Real Time

Live event

From Pedro’s PPT

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conclusions Real-time sentiment analysis based on the consistency

of the user bias Known bias Propagate through endorsements propagate user bias to terms associated with user content combine term bias to computer the overall content polarity

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Thanks &

Question?

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Extra Slides

http://www.cs.cornell.edu/people/pabo/movie-review-data/