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2016 | OriginalPaper | Buchkapitel

Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework

verfasst von : Federica Bisio, Claudia Meda, Paolo Gastaldo, Rodolfo Zunino, Erik Cambria

Erschienen in: Sentiment Analysis and Ontology Engineering

Verlag: Springer International Publishing

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Abstract

Sentiment analysis research has acquired a growing importance due to its applications in several different fields. A large number of companies have included the analysis of opinions and sentiments of costumers as a part of their mission. Therefore, the analysis and automatic classification of large corpora of documents in natural language, based on the conveyed feelings and emotions, has become a crucial issue for text mining purposes. This chapter aims to relate the sentiment-based characterization inferred from books with the distribution of emotions within the same texts. The main result consists in a method to compare and classify texts based on the feelings expressed within the narrative trend.

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Vorheriges Kapitel Hyperelastic-Based Adaptive Dynamics Methodology in Knowledge Acquisition for Computational Intelligence on Ontology Engineering of Evolving Folksonomy Driven Environment

Nächstes Kapitel Interpretability of Computational Models for Sentiment Analysis

http://sentic.net/sentics.

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Titel: Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework
verfasst von: Federica Bisio
Claudia Meda
Paolo Gastaldo
Rodolfo Zunino
Erik Cambria
Verlag: Springer International Publishing
Buch: Sentiment Analysis and Ontology Engineering
Print ISBN: 978-3-319-30317-8

Electronic ISBN: 978-3-319-30319-2

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-30319-2_8

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