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Reducing the Need for Manual Annotated Datasets in Aspect Sentiment Classification by Transfer Learning and Weak-Supervision

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Agents and Artificial Intelligence (ICAART 2020)

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Abstract

Users’ opinions can be greatly beneficial in developing and providing products and services and improving marketing techniques for customer recommendation and retention. For this reason, sentiment analysis algorithms that automatically extract sentiment information from customers’ reviews are receiving growing attention from the computer science community. Aspect-based sentiment analysis (ABSA) allows for a more detailed understanding of customer opinions because it enables extracting sentiment polarities along with the sentiment target from sentences. ABSA consists of two steps: Aspect Extraction (AE) that allows recognizing the target sentiment; Aspect Sentiment Classification (ASC) that enables to classify the sentiment polarity. Currently, most diffused sentiment analysis algorithms are based on deep learning. Such algorithms require large labeled datasets that are extremely expensive and time consuming to build. In this paper, we present two approaches based on transfer learning and weak supervision, respectively. Both have the goal of reducing the manual effort needed to build annotated datasets for the ASC problem. In the paper, we describe the two approaches and experimentally compare them.

This work was supported by Horizon 2020 - Asse I – PON I&C 2014–2020 FESR - Fondo per la Crescita Sostenibile - Sportello Fabbrica Intelligente DM 05/03/2018 – DD 20/11/2018. “Validated Question Answering” Project.

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Notes

  1. 1.

    https://www.yelp.com/dataset/challenge.

  2. 2.

    In [14] the addressed task is a bit different from ASC: the authors classify entity-attribute pair, where entity and attribute belong to predefined lists, e.g. food, price, location for entity and food-price, food-quality for attribute.

  3. 3.

    https://textblob.readthedocs.io/.

  4. 4.

    https://www.clips.uantwerpen.be/pages/pattern-en/.

  5. 5.

    https://www.yelp.com/dataset.

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Authors and Affiliations

  1. High Performance Computing and Networking Institute of the National Research Council (ICAR-CNR), Via Pietro Bucci 8/9C, 87036, Rende, CS, Italy

    Ermelinda Oro & Massimo Ruffolo

  2. altilia.ai, Piazza Vermicelli, Technest, University of Calabria, 87036, Rende, CS, Italy

    Ermelinda Oro, Massimo Ruffolo & Francesco Visalli

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  1. Ermelinda Oro
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  1. LIACC, University of Porto, Porto, Portugal

    Ana Paula Rocha

  2. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

  3. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

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Oro, E., Ruffolo, M., Visalli, F. (2021). Reducing the Need for Manual Annotated Datasets in Aspect Sentiment Classification by Transfer Learning and Weak-Supervision. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2020. Lecture Notes in Computer Science(), vol 12613. Springer, Cham. https://doi.org/10.1007/978-3-030-71158-0_21

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