Chenghua Lin amp Yulan He CIKM09 Main Idea This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation LDA called joint sentiment ID: 398953
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Slide1
Joint Sentiment/Topic Model for Sentiment Analysis
Chenghua
Lin
&
Yulan
He
CIKM09Slide2
Main Idea
This
paper
proposes
a novel probabilistic modeling framework based on
Latent
Dirichlet
Allocation (LDA), called joint sentiment/
topic model
(JST), which detects sentiment and topic
simultaneously
from text.
Slide3
Related Works
Sentiment classification based on Machine Learning (e.g. supervised) requires a large amount of human annotation
Sentiment classification model trained in one domain cannot work well in another domain
topic
/feature detection and sentiment
classification are
often
performed separately, which ignores their mutual dependence.
e.g. ‘unpredictable steering’:
Negative in automobile review
Positive in movie reviewSlide4
JST model
1. Fully unsupervised. No need for human annotation
2. Detect sentiment/topic simultaneously by considering their mutual relation Slide5
LDA vs. JST
LDA
Two Matrices:
D
×
T distribution:
θ
T
× W distribution:
φ
JST
Three Matrices:
D
× S distribution:
π
D
× S × T distribution:
θ
D
× S × W distribution:
φSlide6
LDA vs. JST (cont.)
LDA
JSTSlide7
Process of JSTSlide8
Incorporating Model Priors
One of the directions for improving the
sentiment
detection accuracy is to incorporate prior
information or
subjectivity lexicon (i.e., words bearing positive or
negative
polarity), which can be obtained in many different ways
.
Paradigm word
listMutual informationFull subjectivity
lexiconFiltered subjectivity lexiconSlide9
Experiment
Sentiment Classification: Only consider two sentiment labels, i.e. positive
or negative
Topic ExtractionSlide10
Sentiment ClassificationSlide11
Sentiment Classification (cont.)Slide12
Summary 1
1. Classification performance of JST is very close to the best performance of ML but save a lot of annotation work.
2. topic
information indeed helps in
sentiment classification
as the JST model with the mixture of
topics consistently
outperforms a simple LDA model ignoring
the mixture
of topics.
Slide13
Topic ExtractionSlide14
Summary 2:
Manually
examining the data reveals
that the
terms that seem not conveying sentiments under
these two
topics in fact appear in the context expressing
positive sentiments
. The above analysis illustrates the
effectiveness of
JST in extracting mixture of topics from a corpus.Slide15
Conclusion
1.
presented a joint sentiment/
topic (
JST) model which can detect document level sentiment
and extract
mixture of topics from text simultaneously.
2.
fully unsupervised, thus provides more
flexibilities and
can be easier adapted to other domain.3. yield competitive performance in document level sentiment
classification compared other existing supervised approaches4. discovered
topics
that corresponds to positive/negative sentiment