From Bag-of-Words to Pre-trained Neural Language Models: Improving Automatic Classification of App Reviews for Requirements Engineering

  • Adailton Araujo Universidade de São Paulo
  • Marcos Golo Universidade de São Paulo
  • Breno Viana Universidade de São Paulo
  • Felipe Sanches Universidade de São Paulo
  • Roseli Romero USP-SC
  • Ricardo Marcacini ICMC/USP

Resumo


Popular mobile applications receive millions of user reviews. Thesereviews contain relevant information, such as problem reports and improvementsuggestions. The reviews information is a valuable knowledge source for soft-ware requirements engineering since the analysis of the reviews feedback helpsto make strategic decisions in order to improve the app quality. However, due tothe large volume of texts, the manual extraction of the relevant information is animpracticable task. In this paper, we investigate and compare textual represen-tation models for app reviews classification. We discuss different aspects andapproaches for the reviews representation, analyzing from the classic Bag-of-Words models to the most recent state-of-the-art Pre-trained Neural Languagemodels. Our findings show that the classic Bag-of-Words model, combined witha careful analysis of text pre-processing techniques, is still a competitive model.However, pre-trained neural language models showed to be more advantageoussince it obtains good classification performance, provides significant dimension-ality reduction, and deals more adequately with semantic proximity between thereviews’ texts, especially the multilingual neural language models.

Palavras-chave: opinion mining, sentiment analysis, data-driven requirements engineering, crowd RE, mobile apps, app review, software review, user feedback, natural language processing, automatic classification

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Publicado
20/10/2020
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ARAUJO, Adailton; GOLO, Marcos; VIANA, Breno; SANCHES, Felipe; ROMERO, Roseli; MARCACINI, Ricardo. From Bag-of-Words to Pre-trained Neural Language Models: Improving Automatic Classification of App Reviews for Requirements Engineering. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 378-389. DOI: https://doi.org/10.5753/eniac.2020.12144.