Paper Info
Title
Hierarchical Approach to Sentiment Analysis
Abstract
model to discover and distinguish aspecSentiment analysis aims to extract the customer'sattitude and feeling from his/her unstructured reviews by separatingthe subjective information from the other information.We propose a generative probabilistic topic model that detectsboth an aspect and corresponding sentiment, simultaneously,from review articles. Unlike existing sentiment analysis models,which generally consider rating prediction to be a side task, ourproposal, the hierarchical approach to sentiment analysis, identifiesboth an item and its rating by dividing topics, traditionallytreated as one entity, into aspect and sentiment topics. Since ourmodel is aware of both objective and subjective information, itcan discover fine-grained tightly coherent topics, and describethe generative process of each article in a unified manner. Tohandle the differences involved, HASA extends topic models byintroducing both observed variables and a latent switch variableinto each token, where topics are influenced not only by word cooccurrencebut also item/rating information, and then classifyingthem as either aspect or sentiment topics. Experiments on reviewarticles show that the proposed model is useful as a generativets from sentiments.
Year
DOI
Venue
2012
10.1109/ICSC.2012.62
ICSC
Keywords
Field
DocType
hierarchical approach,rating information,corresponding sentiment,separatingthe subjective information,generative probabilistic topic model,subjective information,sentiment analysis,sentiment analysis model,aspecsentiment analysis,sentiment topic,switches,data mining,data models,market research,predictive models,probability,probabilistic logic
Data modeling,Sentiment analysis,Computer science,Artificial intelligence,Natural language processing,Topic model,Generative grammar,Probabilistic logic,Security token,Market research,Feeling
Conference
Citations 
PageRank 
References 
2
0.39
17
Authors
1
Name
Order
Citations
PageRank
Noriaki Kawamae111910.96