From Sentiment to Reputation ILPS @ RepLab 2012 Hendrike Peetz Maarten de Rijke Anne Schuth University of Amsterdam June 12, 2012 H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 1 / 20
Outline RepLab 2012 1 Motivation by Examples 2 Assumptions and Intuition 3 Model 4 Wrap up 5 H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 2 / 20
Outline Disclaimer Preliminary work H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 3 / 20
Outline Disclaimer Preliminary work Downside rough edges, no results, no conclusions, ... H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 3 / 20
Outline Disclaimer Preliminary work Downside rough edges, no results, no conclusions, ... Upside your feedback is greatly appreciated, you can still join! H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 3 / 20
RepLab 2012 RepLab 2012 An Evaluation Campaign for Online Reputation Management Systems H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 4 / 20
RepLab 2012 RepLab 2012 An Evaluation Campaign for Online Reputation Management Systems Runs at CLEF 2012 H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 4 / 20
RepLab 2012 RepLab 2012 An Evaluation Campaign for Online Reputation Management Systems Runs at CLEF 2012 Tasks: H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 4 / 20
RepLab 2012 RepLab 2012 An Evaluation Campaign for Online Reputation Management Systems Runs at CLEF 2012 Tasks: Monitoring early alerting on issues that may damage (or, in general, alter) the reputation of a company, institution, person, brand, product, etc. H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 4 / 20
RepLab 2012 RepLab 2012 An Evaluation Campaign for Online Reputation Management Systems Runs at CLEF 2012 Tasks: Monitoring early alerting on issues that may damage (or, in general, alter) the reputation of a company, institution, person, brand, product, etc. Profiling mining the reputation of a company as it distills from online media H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 4 / 20
RepLab 2012 RepLab 2012 An Evaluation Campaign for Online Reputation Management Systems Runs at CLEF 2012 Tasks: Monitoring early alerting on issues that may damage (or, in general, alter) the reputation of a company, institution, person, brand, product, etc. Profiling mining the reputation of a company as it distills from online media Manual annotations are provided by online reputation management experts from a major Public Relations consultancy (Llorente & Cuenca) H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 4 / 20
RepLab 2012 RepLab 2012 An Evaluation Campaign for Online Reputation Management Systems Runs at CLEF 2012 Tasks: Monitoring early alerting on issues that may damage (or, in general, alter) the reputation of a company, institution, person, brand, product, etc. Profiling mining the reputation of a company as it distills from online media Manual annotations are provided by online reputation management experts from a major Public Relations consultancy (Llorente & Cuenca) Deadline for runs: July 8th H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 4 / 20
Motivation by Examples Motivation by Examples Prototypical examples “Lehmann Brothers goes bankrupt” Factual statements can have polarity for reputation H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 5 / 20
Motivation by Examples Motivation by Examples Prototypical examples “Lehmann Brothers goes bankrupt” Factual statements can have polarity for reputation “R.I.P. Michael Jackson. We’ll miss you” Negative associated sentiment (sadness), but a positive implication for the reputation of Michael Jackson H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 5 / 20
Motivation by Examples Motivation by Examples Actual labeled data for the brand “Lufthansa” H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 6 / 20
Motivation by Examples Motivation by Examples Actual labeled data for the brand “Lufthansa” Negative “Lufthansa meal on way back - sausage & gerkin on cold mash #nothanks” “Lufthansa is run by retards.” H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 6 / 20
Motivation by Examples Motivation by Examples Actual labeled data for the brand “Lufthansa” Negative “Lufthansa meal on way back - sausage & gerkin on cold mash #nothanks” “Lufthansa is run by retards.” Positive “More flights = leading indicator of econ growth? Lufthansa sees more flights to Vancouver in its future ow.ly/7oLhM” “Lufthansa is growing in Berlin: 30 new destinations from summer 2012- Lufthansa’s Berlin fleet is set t... bit.ly/v2lgHy #airnews” H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 6 / 20
Motivation by Examples Motivation by Examples Actual labeled data for the brand “Lufthansa” Negative “Lufthansa meal on way back - sausage & gerkin on cold mash #nothanks” “Lufthansa is run by retards.” Positive “More flights = leading indicator of econ growth? Lufthansa sees more flights to Vancouver in its future ow.ly/7oLhM” “Lufthansa is growing in Berlin: 30 new destinations from summer 2012- Lufthansa’s Berlin fleet is set t... bit.ly/v2lgHy #airnews” Neutral “I’m at Lufthansa Aviation Center (LAC) (Airportring 1, Frankfurt am Main) w/ 2 others 4sq.com/vTCDiA”a H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 6 / 20
Assumptions and Intuition Assumptions 1 as tweets are rather short, we assume it is on topic as soon as we find a reference to the entity in question; H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 7 / 20
Assumptions and Intuition Assumptions 1 as tweets are rather short, we assume it is on topic as soon as we find a reference to the entity in question; 2 we even assume that all replies stay on topic; H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 7 / 20
Assumptions and Intuition Assumptions 1 as tweets are rather short, we assume it is on topic as soon as we find a reference to the entity in question; 2 we even assume that all replies stay on topic; 3 a tweet with positive (negative) sentiment from a user who tweets mainly negative (positive) tweets has more impact on the reputation; H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 7 / 20
Assumptions and Intuition Assumptions 1 as tweets are rather short, we assume it is on topic as soon as we find a reference to the entity in question; 2 we even assume that all replies stay on topic; 3 a tweet with positive (negative) sentiment from a user who tweets mainly negative (positive) tweets has more impact on the reputation; 4 the impact on reputation of a tweet relates to the number of followers the twitterer has; H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 7 / 20
Assumptions and Intuition Assumptions 1 as tweets are rather short, we assume it is on topic as soon as we find a reference to the entity in question; 2 we even assume that all replies stay on topic; 3 a tweet with positive (negative) sentiment from a user who tweets mainly negative (positive) tweets has more impact on the reputation; 4 the impact on reputation of a tweet relates to the number of followers the twitterer has; 5 positive sentiment can cancel negative sentiment and vice versa; positive reputation can cancel negative reputation and vice versa. H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 7 / 20
Assumptions and Intuition Assumptions 1 as tweets are rather short, we assume it is on topic as soon as we find a reference to the entity in question; 2 we even assume that all replies stay on topic; 3 a tweet with positive (negative) sentiment from a user who tweets mainly negative (positive) tweets has more impact on the reputation; 4 the impact on reputation of a tweet relates to the number of followers the twitterer has; 5 positive sentiment can cancel negative sentiment and vice versa; positive reputation can cancel negative reputation and vice versa. 6 the impact on the reputation of an entity as represented in a tweet is based on the sentiment it solicits in other users; H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 7 / 20
Assumptions and Intuition Intuition Sentiment of reactions contribute to the polarity: “R.I.P. Michael Jackson” “R.I.P. Lehmann Brothers” H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 8 / 20
Assumptions and Intuition Intuition Sentiment of reactions contribute to the polarity: “R.I.P. Michael Jackson” “R.I.P. Lehmann Brothers” “I love you MJ!” “Michael was the greatest artist ever” “Will miss you” “Indeed, R.I.P” H. Peetz, M. de Rijke, A. Schuth (UvA) From Sentiment to Reputation June 12, 2012 8 / 20
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