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Erschienen in:
Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A Unified Probabilistic Model for Aspect-Level Sentiment Analysis

verfasst von : Daniel Stantic, Fei Song

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Aspect-level sentiment analysis aims to delve deep into opinionated text to discover sentiments expressed about specific aspects of the discussed topics. Aspect detection is often achieved by topic modelling. Probabilistic modelling has been one of the more popular approaches for both topic modelling and sentiment analysis. Incorporating Part-Of-Speech (POS) information and modelling the emphasis placed on each topic have been shown to improve the quality of such models. Previous approaches to aspect-level sentiment analysis typically model only some of these components or rely on external tools or resources to provide some of the information. In this paper, we develop a new, unified probabilistic model that can capture topics, topic weights, syntactic classes, and sentiment levels from unstructured text without relying on any external sources of information. Our solution builds on the ideas of the existing probabilistic models but generalizes them into a unified framework with some novel extensions.

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Vorheriges Kapitel Cross-Lingual Entity Matching for Heterogeneous Online Wikis
Nächstes Kapitel An Empirical Study on Learning Based Methods for User Consumption Intention Classification
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Metadaten
Titel
A Unified Probabilistic Model for Aspect-Level Sentiment Analysis
verfasst von
Daniel Stantic
Fei Song
Copyright-Jahr
2018
Buch
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-319-73617-4

Electronic ISBN: 978-3-319-73618-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-73618-1_79