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Sentiment Analysis and Ontology Engineering pp 175–197Cite as

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

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 639))

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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Notes

  1. 1.

    http://sentic.net/sentics.

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Author information

Authors and Affiliations

  1. DITEN – University of Genoa, Via Opera Pia 11A, 16145, Genoa, Italy

    Federica Bisio, Claudia Meda, Paolo Gastaldo & Rodolfo Zunino

  2. SCE – School of Computer Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore

    Erik Cambria

Authors
  1. Federica Bisio
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  2. Claudia Meda
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  3. Paolo Gastaldo
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  4. Rodolfo Zunino
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  5. Erik Cambria
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Correspondence to Federica Bisio .

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Editors and Affiliations

  1. Department of Electrical and Computer, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. and Technology, National Taiwan University of Scien, Taipei, Taiwan

    Shyi-Ming Chen

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Bisio, F., Meda, C., Gastaldo, P., Zunino, R., Cambria, E. (2016). Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework. In: Pedrycz, W., Chen, SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-30319-2_8

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