TY - GEN
T1 - Classification and detection of micro-level impact of issue-focused documentary films based on reviews
AU - Rezapour, Rezvaneh
AU - Diesner, Jana
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/2/25
Y1 - 2017/2/25
N2 - We present novel research at the intersection of review mining and impact assessment of issue-focused information products, namely documentary films. We develop and evaluate a theoretically grounded classification schema, related codebook, corpus annotation, and prediction model for detecting multiple types of impact that documentaries can have on individuals, such as change versus reaffirmation of behavior, cognition, and emotions, based on user-generated content, i.e., reviews. This work broadens the scope of review mining tasks, which typically comprise the prediction of ratings, helpfulness, and opinions. Our results suggest that documentaries can change or reinforce peoples' conception of an issue. We perform supervised learning to predict impact on the sentence level by using data driven as well as predefined linguistic, lexical, and psychological features; achieving an accuracy rate of 81% (F1) when using a Random Forest classifier, and 73% with a Support Vector Machine.
AB - We present novel research at the intersection of review mining and impact assessment of issue-focused information products, namely documentary films. We develop and evaluate a theoretically grounded classification schema, related codebook, corpus annotation, and prediction model for detecting multiple types of impact that documentaries can have on individuals, such as change versus reaffirmation of behavior, cognition, and emotions, based on user-generated content, i.e., reviews. This work broadens the scope of review mining tasks, which typically comprise the prediction of ratings, helpfulness, and opinions. Our results suggest that documentaries can change or reinforce peoples' conception of an issue. We perform supervised learning to predict impact on the sentence level by using data driven as well as predefined linguistic, lexical, and psychological features; achieving an accuracy rate of 81% (F1) when using a Random Forest classifier, and 73% with a Support Vector Machine.
KW - Micro-level impact
KW - Natural language processing
KW - Review mining
KW - Social impact assessment
KW - Supervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85014780759&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014780759&partnerID=8YFLogxK
U2 - 10.1145/2998181.2998201
DO - 10.1145/2998181.2998201
M3 - Conference contribution
AN - SCOPUS:85014780759
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 1419
EP - 1431
BT - CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
PB - Association for Computing Machinery
T2 - 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017
Y2 - 25 February 2017 through 1 March 2017
ER -