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Article
Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian
by Rosario Catelli, Serena Pelosi and Massimo Esposito
Electronics 2022, 11(3), 374; https://doi.org/10.3390/electronics11030374 - 26 Jan 2022
Cited by 122 | Viewed by 14979
Abstract
Recent evolutions in the e-commerce market have led to an increasing importance attributed by consumers to product reviews made by third parties before proceeding to purchase. The industry, in order to improve the offer intercepting the discontent of consumers, has placed increasing attention [...] Read more.
Recent evolutions in the e-commerce market have led to an increasing importance attributed by consumers to product reviews made by third parties before proceeding to purchase. The industry, in order to improve the offer intercepting the discontent of consumers, has placed increasing attention towards systems able to identify the sentiment expressed by buyers, whether positive or negative. From a technological point of view, the literature in recent years has seen the development of two types of methodologies: those based on lexicons and those based on machine and deep learning techniques. This study proposes a comparison between these technologies in the Italian market, one of the largest in the world, exploiting an ad hoc dataset: scientific evidence generally shows the superiority of language models such as BERT built on deep neural networks, but it opens several considerations on the effectiveness and improvement of these solutions when compared to those based on lexicons in the presence of datasets of reduced size such as the one under study, a common condition for languages other than English or Chinese. Full article
(This article belongs to the Special Issue Deep Learning and Explainability for Sentiment Analysis)
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