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The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9042))

Abstract

Hitherto, sentiment analysis has been mainly based on algorithms relying on the textual representation of online reviews and microblogging posts. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling, and counting their words. But when it comes to interpreting sentences and extracting opinionated information, their capabilities are known to be very limited. Current approaches to sentiment analysis are mainly based on supervised techniques relying on manually labeled samples, such as movie or product reviews, where the overall positive or negative attitude was explicitly indicated. However, opinions do not occur only at document-level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a review. In order to overcome this and many other issues related to sentiment analysis, we propose a novel framework, termed concept-level sentiment analysis (CLSA) model, which takes into account all the natural-language-processing tasks necessary for extracting opinionated information from text, namely: microtext analysis, semantic parsing, subjectivity detection, anaphora resolution, sarcasm detection, topic spotting, aspect extraction, and polarity detection.

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Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria, Soujanya Poria, Rajiv Bajpai & Iti Chaturvedi

  2. DITEN, University of Genoa, Genoa, Italy

    Federica Bisio

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  1. Erik Cambria
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  2. Soujanya Poria
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  3. Federica Bisio
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  4. Rajiv Bajpai
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  5. Iti Chaturvedi
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Correspondence to Erik Cambria .

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  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico DF, Mexico

    Alexander Gelbukh

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Cambria, E., Poria, S., Bisio, F., Bajpai, R., Chaturvedi, I. (2015). The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_1

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