Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian

Citations Over TimeTop 1% of 2022 papers

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 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.

Related Papers

• → Sentiment analysis in a cross-media analysis framework(2016)70 cited
• → Intelligent sentinet-based lexicon for context-aware sentiment analysis: optimized neural network for sentiment classification on social media(2021)32 cited
• → Comparative Analysis of Lexicon and Machine Learning Approach for Sentiment Analysis(2022)24 cited
• → Sinhala Sentiment Analysis using Corpus based Sentiment Lexicon(2019)22 cited
• → Construction Method of Sentiment Lexicon Based on Word2vec(2019)6 cited