Social Data Sentiment Analysis
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Transcript of Social Data Sentiment Analysis
Social Sentiment Analysis in Social Data
Seth Grimes Alta Plana Corporation
@sethgrimes
Social Data and Analytics – DC March 11, 2015
Social Sentiment Analysis in Social Data
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http://altaplana.com/TA2014
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News in the last week…
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technewstoday.com
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Social Data? • Profiles. • Identity, categories.
• Connections. • Elements: Directionality, degree.
• Content: Text, photos, videos, • Actions: Likes, Clicks, Shares… • Elements: Time, location, target, sequence.
Sentiment can be extracted or inferred from all of these. How?
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Analytics is the systematic application of algorithmic methods that derive and deliver information, typically expressed quantitatively, whether in the form of indicators, tables, visualizations, or models. • Systematic means formal & repeatable. • Algorithmic contrasts with heuristic.
Analytics creates and/or applies models.
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Sentiment – • Opinion, attitude, emotion, and mood:
Affective states. • Communicated via expressions and actions. • Understood contextually. • Applied situationally.
Anyone who tells you that sentiment analysis is solely about computing a positive/negative/neutral(/mixed) evaluation is probably hawking a weak solution.
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The sentiment value of an opinion may be expressed as a quintuple (oj, fjk, soijkl, hi, tl): • oj is a target object. • fjk is an feature of the object oj. • hi is an opinion holder. • tl is the time when the opinion is expressed. • soijkl is the sentiment value of the opinion of the opinion holder hi regarding feature fjk of object oj at time tl.
• soijkl is +ve, -‐ve, or neu, or a more granular rating.
Bing Liu, NLP Handbook
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Comparative opinions -‐-‐ (O1, O2, F, po, h, t): • O1 and O2 are object sets being compared based on shared features F.
• po is the preferred object set of the opinion holder h.
• t is the time when the comparative opinion is expressed.
Bing Liu
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Questions for business (& government): What are people saying? What’s hot/trending? What are they saying about {topic|person|product} X? ... about X versus {topic|person|product} Y? How has opinion about X and Y evolved? How has opinion correlated with {our|competitors’|general} {news|marketing|sales|events}?
Who (and What, When & How) are opinion leaders? How does sentiment propagate across multiple channels? What’s behind opinion, the root causes?
(How) Can we link opinions, profiles, behaviors & transactions to discern intent and predict actions?
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Measurement methods – • Polling, surveys including NPS. • Natural language processing. • Action stats. • Network and information-‐flow analysis. • Biometrics.
Modelled concepts – • Affinities (clusters). • Influence, Authority. • Satisfaction, Loyalty, Motivation. • Likelihood to buy, Churn propensity...
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Current, 33%
Current, 31%
Current, 34%
Current, 47%
Current, 51%
Current, 56%
Current, 47%
Current, 54%
Current, 66%
Expect, 21%
Expect, 24%
Expect, 23%
Expect, 23%
Expect, 28%
Expect, 25%
Expect, 33%
Expect, 28%
Expect, 22%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Events
Semantic annotations
Other entities – phone numbers, part/product numbers, e-‐mail & street addresses, etc.
Metadata such as document author, publication date, title, headers, etc.
Concepts, that is, abstract groups of entities
Named entities – people, companies, geographic locations, brands, ticker symbols, etc.
Relationships and/or facts
Sentiment, opinions, attitudes, emotions, perceptions, intent
Topics and themes
Do you currently need (or expect to need) to extract or analyze...
http://altaplana.com/TA2014
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Many options (text).
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Emotion and outcomes
Social Sentiment Analysis in Social Data
Seth Grimes Alta Plana Corporation
@sethgrimes
Social Data and Analytics – DC March 11, 2015