Paper Info
Title
Opinion retrieval through unsupervised topological learning
Abstract
Opinion Mining is the field of computational study of peopel's emotional behavior expressed in text. The purpose of this article is to introduce a new framework for emotion (opinion) mining based on topological unsupervised learning and hierarchical clustering. In contrast to supervised learning, the problem of clustering characterization in the context of opinion mining based on unsupervised learning is challenging, because label information is not available or not used to guide the learning algorithm. The algorithm described in this paper provides topological clustering of the opionon issued from the tweets, each cluster being associated to a prototype and a weight vector, reflecting the relevance of the data belonging to each clsuter. The proposed framework requires simple computational techniques and are based on the double local weighting self-organizing map (dlw-SOM) model and Hierarchical Clustering. The proposed framework has been used on a real dataset issued from the tweets collected during the 2012 French election compaign.
Year
DOI
Venue
2014
10.1109/IJCNN.2014.6889934
Neural Networks
Keywords
Field
DocType
emotion recognition,information retrieval,pattern clustering,self-organising feature maps,social networking (online),text analysis,unsupervised learning,dlw-SOM model,double local weighting self-organizing map,emotion mining,hierarchical clustering,label information,learning algorithm,opinion mining,opinion retrieval,people emotional behavior,topological clustering,topological unsupervised learning,tweets,unsupervised topological learning
Competitive learning,Pattern recognition,Computer science,Unsupervised learning,Artificial intelligence,Conceptual clustering,Cluster analysis,Machine learning
Conference
ISSN
ISBN
Citations 
2161-4393
978-1-4799-6627-1
0
PageRank 
References 
Authors
0.34
13
2
Name
Order
Citations
PageRank
Nicoleta Rogovschi1408.42
Nistor Grozavu26716.76