@Article{2019.100000152,
AUTHOR = {PCD Kalaivaani, Dr. R Thangarajan},
TITLE = {Enhancing the Classification Accuracy in Sentiment Analysis with  Computational Intelligence Using Joint Sentiment Topic Detection with  MEDLDA},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {1},
PAGES = {71--79},
URL = {http://www.techscience.com/iasc/v26n1/39857},
ISSN = {2326-005X},
ABSTRACT = {Web mining is the process of integrating the information from web by 
traditional data mining methodologies and techniques. Opinion mining is an 
application of natural language processing to extract subjective information 
from web. Online reviews require efficient classification algorithms for analysing 
the sentiments, which does not perform an in–depth analysis in current 
methods. Sentiment classification is done at document level in combination with 
topics and sentiments. It is based on weakly supervised Joint Sentiment-Topic 
mode which extends the topic model Maximum Entropy Discrimination Latent 
Dirichlet Allocation by constructing an additional sentiment layer. It is assumed 
that topics generated are dependent on sentiment distributions and the words 
generated are conditioned on the sentiment topic pairs. MEDLDA is used to 
increase the accuracy of topic modeling.},
DOI = {10.31209/2019.100000152}
}