1,333 research outputs found
A Bayesian-Based Approach for Public Sentiment Modeling
Public sentiment is a direct public-centric indicator for the success of
effective action planning. Despite its importance, systematic modeling of
public sentiment remains untapped in previous studies. This research aims to
develop a Bayesian-based approach for quantitative public sentiment modeling,
which is capable of incorporating uncertainty and guiding the selection of
public sentiment measures. This study comprises three steps: (1) quantifying
prior sentiment information and new sentiment observations with Dirichlet
distribution and multinomial distribution respectively; (2) deriving the
posterior distribution of sentiment probabilities through incorporating the
Dirichlet distribution and multinomial distribution via Bayesian inference; and
(3) measuring public sentiment through aggregating sampled sets of sentiment
probabilities with an application-based measure. A case study on Hurricane
Harvey is provided to demonstrate the feasibility and applicability of the
proposed approach. The developed approach also has the potential to be
generalized to model various types of probability-based measures
Modeling Word Burstiness Using the Dirichlet Distribution
Multinomial distributions are often used to model text documents. However, they do not capture well the phenomenon that words in a document tend to appear in bursts: if a word appears once, it is more likely to appear again. In this paper, we propose the Dirichlet compound multinomial model (DCM) as an alternative to the multinomial. The DCM model has one additional degree of freedom, which allows it to capture burstiness. We show experimentally that the DCM is substantially better than the multinomial at modeling text data, measured by perplexity. We also show using three standard document collections that the DCM leads to better classification than the multinomial model. DCM performance is comparable to that obtained with multiple heuristic changes to the multinomial model. 1
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