Paper Info
Title
Classification and Detection of Micro-Level Impact of Issue-Focused Documentary Films based on Reviews.
Abstract
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.
Year
DOI
Venue
2017
10.1145/2998181.2998201
CSCW
Keywords
Field
DocType
Social Impact Assessment, Micro-level Impact, Review Mining, Natural Language Processing, Supervised Machine Learning
Impact assessment,Helpfulness,Computer science,Support vector machine,Supervised learning,Artificial intelligence,Natural language processing,Random forest,Sentence,Schema (psychology),Social impact assessment
Conference
Citations 
PageRank 
References 
1
0.36
14
Authors
2
Name
Order
Citations
PageRank
Rezvaneh Rezapour175.19
Jana Diesner221624.38