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Compressing Bayesian Networks: Swarm-Based Descent, Efficiency, and Posterior Accuracy Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Sentence-Level Sparse Gamma Topic Model for Sentiment Analysis

Authors : Tao Chen, Jeffrey Parsons

Published in: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Online consumer reviews have become an essential source of information for understanding markets and customer preferences. This research introduces a novel topic model to identify product attributes and sentiments toward them at the sentence level. The model uses a recursive definition of topic distribution in a sentence to avoid the problem of over-parametrization in topic models. The introduction of the inference network enables the utilization of rich features in the content to drive the identification of sentiments, in contrast with other multi-aspect sentiment analysis models that rely on single words. The sentence topic model has a superior performance in producing coherent topics, and the sentence topic-sentiment model outperforms the existing model on the task of predicting product attribute rating.

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previous chapter Towards a Comprehensive Evaluation of Recommenders: A Cognition-Based Approach
next chapter Topic Detection and Document Similarity on Financial News
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Metadata
Title
A Sentence-Level Sparse Gamma Topic Model for Sentiment Analysis
Authors
Tao Chen
Jeffrey Parsons
Copyright Year
2018
Book
Advances in Artificial Intelligence

Print ISBN: 978-3-319-89655-7

Electronic ISBN: 978-3-319-89656-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-89656-4_33

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