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

• Published in: Agents and Artificial Intelligence Vol. 12613; pp. 445 - 464
• Main Authors: Oro, Ermelinda, Ruffolo, Massimo, Visalli, Francesco
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Aspect based sentiment analysis; Aspect sentiment classification; BERT; Data programming; Deep learning; Fine-tuning; Natural language processing; Post-trained language model; Post-training; Sentiment analysis; Transfer learning; Transformers; Weak-supervision
• ISBN: 9783030711573; 3030711579
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-71158-0_21

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.