Sentiment Analysis with Global Topics and Local Dependency
Date
2010-07
Authors
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Journal ISSN
Volume Title
Publisher
Association for the Advancement of Artificial Intelligence (AAAI), Palo Alto, California
Abstract
With the development of Web 2.0, sentiment analysis has
now become a popular research problem to tackle. Recently,
topic models have been introduced for the simultaneous
analysis for topics and the sentiment in a document. These
studies, which jointly model topic and sentiment, take the
advantage of the relationship between topics and sentiment,
and are shown to be superior to traditional sentiment
analysis tools. However, most of them make the assumption
that, given the parameters, the sentiments of the words in
the document are all independent. In our observation, in
contrast, sentiments are expressed in a coherent way. The
local conjunctive words, such as “and” or “but”, are often
indicative of sentiment transitions.
In this paper, we propose a major departure from the
previous approaches by making two linked contributions.
First, we assume that the sentiments are related to the topic
in the document, and put forward a joint sentiment and topic
model, i.e. Sentiment-LDA. Second, we observe that
sentiments are dependent on local context. Thus, we further
extend the Sentiment-LDA model to Dependency-
Sentiment-LDA model by relaxing the sentiment
independent assumption in Sentiment-LDA. The sentiments
of words are viewed as a Markov chain in Dependency-
Sentiment-LDA. Through experiments, we show that
exploiting the sentiment dependency is clearly advantageous,
and that the Dependency-Sentiment-LDA is an effective
approach for sentiment analysis.
Description
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10)
item.page.type
Conference Paper
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Keywords
SENTIMENT ANALYSIS, ARTIFICIAL INTELLIGENCE, MARKOV CHAINS
Citation
LI, F., Huang, M., & Zhu, X. (2010). Sentiment Analysis with Global Topics and Local Dependency. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10). (p. 1371-1376). Association for the Advancement of Artificial Intelligence.