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Rede Neural Convoluci= onal aplicada na Análise de Sentimentos em Comentários de Clientes de Empresa do= Ramo Varejista

Convolutional Neural Network applied to Sentiment Analysis in Customer Comments of a Reta= il Company

 =

Mari= a Sheila Carneiro

https://orcid.org/0000-0002-6890-2276

= Mestre em Informática e Gestão do Conhecimento= . UNINOVE – Brasil.  msheilacarneiro@uni9.e= du.br

Dacyr Dante de Oliveira Gatto<= o:p>

https://orcid.org/0000-0003-2146-4819<= /span>

Doutor em Informática e Gestão do Conhecimento. UNINOVE – Brasil.  dacyr.gatto@uni9.pro.br

Renato José Sassi

https://orcid.org/0000-0001-5276-4895<= /span>

Doutor em Engenharia Elétrica. EPUSP – Brasil. sassi@uni9.pro.br

Marcos Antonio Gaspar

https:/= /orcid.org/0000-0002-2422-2455

Doutor em Administração – USP – Brasil. marcos.antonio@uni9.pro.br

 

RESUMO

A Anál= ise de Sentimentos é utilizada no processamento de linguagem humana para detectar opiniões, emoções ou atitudes positivas, negativas ou neutras em textos. Ela permite que empresas compreendam as emoções expressas pelos clientes por tr= ás das palavras ao abrir possibilidades que vão desde melhorias no atendimento= até a apoiar a gestão do cliente. A Rede Neural Convolucio= nal (RNC) é uma técnica da Inteligência Artificial (IA) aplicável a este tipo de análise. Assim, o objetivo deste trabalho foi aplicar RNC = na Análise de Sentimentos em comentários de clientes de uma empresa do ramo varejista para apoiar a gestão do cliente. Em adição, comparou-s= e os resultados dos experimentos com os resultados dos indicadores detratores do= NPS (Net Promoter Score) da empresa. Para atingir o objetivo acima foi selecionada uma base de dados de atendimento aos clientes. Os principais resultados da análise estão relacionados a determinados aspectos dos produt= os e serviços da empresa, dentre os quais destacam-se: cartão, limite, pagamento, aumento e dificuldade de atendimento nos canais disponibilizados pela empre= sa. Quanto ao cruzamento dos atributos relativos ao perfil do cliente com os resultados de NPS foi possível identificar os comentários e principais argumentos segregados por gênero, faixa etária, nível de renda e localidade= de domicílio de clientes com NPS detratores. Conclui-se que foi possível conhe= cer o cliente a partir dos comentários elaborados pelos mesmos e que a solução desenvolvida é capaz de auxiliar na gestão do cliente em empresas varejista= s.

Palavras-chave: avaliação de clie= ntes; análise de sentimentos; varejo; Net Promoter Score; descobert= a de conhecimento.

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ABSTRACT

Sentiment Analysis is used in natural language processing to detect positive, negative, or neutral opinions, emotions, or attitudes in texts. It allows companies to understand the emotions expresse= d by customers behind the words, opening possibilities ranging from customer ser= vice improvements to supporting customer management. The Convolutional Neural Network (CNN) is an Artificial Intelligence (AI)technique applicable to this type of analysis. Thus, the objective of this study was to apply CNN in Sentiment Analysis on customer comments from a retail company to support customer management. Additionally, the results of the experiments were comp= ared with the company's NPS (Net Promoter Score) detractor indicators. To achieve the above objective, a customer service database was selected. The main res= ults of the analysis are related to certain aspects of the company's products and services, including card, limit, payment, increase, and difficulty in acces= sing the company’s service channels. Regarding the correlation between customer profile attributes and NPS results, it was possible to identify comments and main arguments segmented by gender, age group, income level, and location of residence of customers with NPS detractors. It was concluded that it was possible to understand the customer based on the comments made by them and = that the developed solution can assist customer management in retail companies.<= o:p>

Keywords: customer reviews; sentiment analysis; retail; Net Promoter Score; knowledge discovery.

 

 

Recebido em 21/09/2024.  Aprovado em 04/02/2= 025. Avaliado pelo sistema double blind peer review. Publicado conforme normas da ABNT.

https://doi.org/10.22279/navus.v16.202= 8  =

1 INTRODUÇÃO

 

Para se dest= acarem no mercado competitivo, as empresas que atuam no varejo digital precisam ad= otar estratégias de diferenciação que utilizem a Inteligência Artificial (IA). E= la disponibiliza ferramentas eficazes para aprimorar a experiência do cliente, otimizar processos operacionais e se ajustar rapidamente às mudanças, permitindo que as empresas se estabeleçam como líderes no setor (Baehre et al., 2022).

Javaid et al. (2022) afirmam que a IA pode proporcionar oportunidades significativas para inovação e aprimoramento no varejo digital, ao apoiar e gerar novas formas de se relacionar com o cliente das mídias sociais. O uso= da IA permite a criação de experiências de compra personalizadas que sugerem produtos com base no histórico e nas preferências dos clientes.

Assim, conhe= cer os clientes é fundamental para o sucesso de uma empresa que atua no varejo digital, isto porque pode proporcionar a adaptação de produtos e serviços às suas necessidades. Uma forma de viabilizar tal desafio é realizar a análise= dos sentimentos dos clientes buscando extrair informações úteis para a geração = de conhecimento sobre ele (Wankhade; Rao; Kulkarni, 2022).

A Análise de Sentimentos, conforme Chandrasekaran, Nguyen e = Hemanth (2021) é uma área da IA que facilita a identificação e quantificação de emoções em textos e mídias, ajudando a entender a opinião pública, orientar decisões de marketing e melhora= r a interação com clientes digitais.

Na visão dos= autores Meena, Mohbey e Kum= ar (2023) a Análise de Sentimentos tornou-se essencial para as empresas, pois revela as percepções dos clientes sobre produtos e serviços, fortalece relacionamentos e fidelidade, aprimora o atendimento ao cliente e apoia estratégias de no varejo digital.

A Rede Neura= l Convolucional (RNC) é uma técnica da IA que pode ser aplicada na análise de sentimentos, devido à sua capacidade de extrair e representar características textuais de maneira eficiente e eficaz (Cao = et al., 2021).

De acordo, c= om Klaus e Maklan (2021), para avaliar a satisfação e a fidelidade do cliente, as empresas utilizam a metodologia NPS (Net Promo= ter Score). Ainda segundo o autor, o principal objetivo da metodologia NPS é definir uma pergunta simples que possa ajudar as organizações a construírem relacionamentos e satisfação com seus clientes, de modo a proporcionar à empresa o gerenciamento do relacionamento com seus clientes e planejar estrategicamente seu crescimento. A aplicação da ferramenta NPS ilustra a relação diretamente proporcional entre qualidade do relacionamento/serviço e crescimento organizacional.

Comparar os resultados obtidos da aplicação de uma técnica da IA com uma metodologia que avalia a satisfação do cliente é importante para avaliar se as respostas dos experimentos são semelhantes as respostas obtidas a partir da aplicação da metodologia. Esta comparação pode fornecer uma visão mais completa e detalh= ada do sentimento dos clientes, ajudando a tomar decisões mais informadas e a melhorar a experiência do cliente de forma mais eficaz.

Assim, com b= ase no contexto apresentado, o objetivo deste trabalho foi aplicar RNC na Análise = de Sentimentos em comentários de clientes de uma empresa do ramo varejista para apoiar a gestão do cliente.

 

2 REFERENCIAL T= EÓRICO

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Nesta seção = são descritos os conceitos fundamentais da Análise de Sentimentos e das RNCs.

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2.1 Análise de Sentimentos

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As mídias so= ciais possibilitam às empresas uma comunicação direta e imediata com os clientes, promovendo interações personalizadas que fortalecem o relacionamento. Elas fornecem um canal digital valioso para feedback e insights, ajudando a ajustar estratégias de marketing e a desenvolver novos produtos. As plataformas digitais oferecem dados e análises para otimizar campanhas em tempo real, possibilitam experiências personalizadas e permite= m um gerenciamento eficaz de crises e reputação (Dwivedi et al., 2021).

Segundo Antonelo e Lima (2021), as mídias sociais fortalecem a conexão direta e personalizada com os consumidores, possibilitando o engajamento e a lealdade à marca. Além disso, campanhas e promoções nas red= es sociais podem aumentar as vendas, e o feedback obtido oferece ins= ights valiosos sobre o comportamento dos consumidores. Uma presença ativa nas míd= ias sociais também contribui para consolidar a identidade da marca, promovendo = seus valores e diferenciando-a da concorrência.

Entretanto, a geração de grandes quantidades de dados não é, por si só, suficiente para q= ue a empresa compreenda melhor seu cliente. Isso ocorre porque, quando analisados isoladamente ou em seu formato bruto, esses dados não são capazes de gerar conhecimento significativo para a empresa.

Não obstante= , o conjunto desses dados tem enorme potencial para geração de conhecimentos. J= ahani, Jain e Ivanov (2023), afirmam que essas análises podem ser realizadas por m= eio de técnicas de IA essenciais para transformar volumes de dados em informaçõ= es úteis e acionáveis, apoiando a gestão do cliente.

Nesse contex= to, de acordo com Nandwani e Verm= a (2023), a Análise de Sentimentos, uma área da IA possibilita que empresas entendam as emoções do cliente em relação a produtos, serviços e marcas. Es= se entendimento auxilia na adaptação das estratégias e na melhoria da satisfaç= ão do cliente.

A Análise de Sentimentos permite identificar áreas para inovação e aprimoramento, além de monitorar a percepção pública em tempo real para respostas rápidas e detect= ar tendências para estratégias de marketing. Também fornece dados qualitativos= que complementam análises quantitativas, apoia pesquisas acadêmicas e sociais, e permite a personalização das interações com clientes (Liu, 2023).

Dessa forma,= muitas empresas têm valorizado este tipo de análise de opinião, não apenas por sua rentabilidade, mas também porque os resultados obtidos são altamente releva= ntes para a aceitação de um produto, serviço ou imagem corporativa. Assim, os da= dos são coletados sem a necessidade de entrevistas diretas com os autores dos comentários, o que facilita a coleta e a interpretação das opiniões postadas sobre um tema específico (Cho et al= ., 2020).

Os dados col= etados podem ter diferentes origens e devem ser armazenados e analisados posteriormente. Na etapa de pré-processamento, os dados são verificados e padronizados por meio de algoritmos que organizam as palavras e termos presentes nos textos produzidos pelos clientes. Wankha= de, Rao e Kulkarni (2022) abordam três níveis princ= ipais de análise de sentimentos:

 

·       Nível de documento: avalia o sentimen= to geral de um texto completo, oferecendo uma visão ampla das emoções predominantes, mas pode não captar detalhes específicos.

·       Nível de sentença: analisa sentimento= s em sentenças individuais, proporcionando uma visão detalhada e contextual das emoções expressas.

·       Nível de entidade e aspecto: examina sentimentos sobre entidades e características específicas, permitindo uma análise detalhada dos aspectos destacados em um texto.

A avaliação = dos sentimentos expressos em textos ou comunicações pode identificar padrões que preveem dificuldades ou sucessos futuros. São propostas, então, duas aborda= gens para a análise de sentimentos: uma do ponto de vista dos autores, que transmitem emoções e sentimentos através das palavras, e outra do ponto de vista dos leitores, que interpretam essas emoções e sentimentos ao ler o te= xto.

A avaliação = dos sentimentos expressos em textos ou comunicações pode identificar padrões que podem prever dificuldades ou sucessos futuros. Dessa forma, são propostas d= uas abordagens para a análise de sentimentos: uma do ponto de vista dos autores, que transmitem emoções e sentimentos por meio das palavras, e outra do pont= o de vista dos leitores, que interpretam essas emoções e sentimentos ao lerem o texto.

De acordo co= m Kit e Mokji (2022), a Análise de Sentimentos desempenha um = papel fundamental na compreensão das opiniões e emoções dos usuários em relação a produtos e serviços, possibilitando que as empresas melhorem a experiência = do cliente e ajustem suas estratégias com base no feedback recebido. Es= se processo também contribui para o monitoramento da percepção da marca e a ge= stão da reputação online, além de identificar tendências e padrões nas opiniões ao longo do tempo.

A utilização de modelos pré-treinados para a anál= ise automatizada economiza tempo e recursos, tornando o processo mais eficiente= e viável para análises em larga escala. Além disso, as informações obtidas auxiliam no alinhamento das decisões estratégicas às necessidades e expecta= tivas dos clientes. Por esse motivo, é importante considerar o uso de uma técnica= da IA, como a Rede Neural Convolucional (RNC), na análise de sentimentos (Meena, Mohbey e Kumar, 2023).

 

2.2 Redes Neura= is Convolucionais

 

Redes Neurais Convolucionais (RNCs) são um t= ipo de arquitetura de rede neural artificial empregada na classificação de padrões bidimensionais, sendo compostas por três camadas principais, cada uma respo= nsável por funções matemáticas específicas que aprimoram a precisão da classificaç= ão final (Kim; Park, 2024).

De acordo com Raiaan et al., (2024), as RNCs são formadas por várias camadas convolucionais que extraem características como bordas e texturas, nas primeiras camadas, e características como formas e padrões, nas camadas mais profundas. Esses filtros destacam aspectos importantes, como cor e textura, indicativos do grau de maturação.

Cada imagem passa por processos como convolu= ção com filtros (Kernels), pooling, camadas totalmente conectadas= e a função ReLU. Quando aplicadas a textos, como opiniões de clientes, essas técnicas exigem a conversão dos textos em matrizes numéricas para viabiliza= r a convolução.

A camada de agrupamento reduz as dimensões d= os mapas de características utilizando funções como Max Pooling e Av= erage Pooling, que identificam os valores máximos e médios, respectivamente. = Isso resulta em uma redução pela metade da altura e largura da imagem original, permitindo a compactação de cada bloco de 4 pixels em um único pixel sem pe= rda significativa de dados (Raiaan et al., 2024).<= /span>

A camada totalmente conectada, também conhec= ida como Fully Connected Layer (FCL), é responsável pela classificação d= os dados utilizando funções como Softmax para múltiplas classes e Si= gmoid para classes binárias. Nessa etapa, a matriz é “achatada” em um vetor, e os vetores resultantes são processados pela camada totalmente conectada para formar o modelo. Finalmente, a função de ativação Softmax ou Sigm= oid é aplicada para classificar as saídas (Raiaan et al., 2024).

Uma revisão sistemática de técnicas de otimização de hiperparâmetr= os para RNCs é apresentada no trabalho de Raiaan, et al= . (2024).

A aplicação eficaz da RNC na análise de sentimentos em comentários pode ser comprovada nos trabalhos de Alawi e Bozkurt (2024), <= span class=3DSpellE>Sharbatian e Moattar (2023), Li, et al., (202= 3), Wang, et al., (2021), Nandwani e Verma (2021) e Wankhade, et al., (2022).

RNCs também apresentam bons resultados quando aplicadas em sistemas de recomenda= ção (Almaghrabi; Chetty, 2020), na redução da lacuna entre as experiências de varejo online e offlin= e (Srivastava; et al., 2024), na previsão de preços do mercado varejista (Kim; Park, 2024= ), e na previsão de vendas de automóveis (Sivabalan<= /span>; Minu, 2025).

 

3 MÉTODOS E INSTRUMENTOS DE PESQUISA

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Nesta seção a tipologia de pesquisa, a estrutura da base de dados de atendimento e as fas= es que compõem a metodologia experimental são apresentadas.

 

3.1 Tipologia da Pesquisa

 

A tipologia de pesquisa é composta pelas pes= quisas bibliográfica, exploratória e experimental. A pesquisa bibliográfica foi re= alizada com base em consultas a fontes de referência bibliográficas e teóricas, tais como artigos, livros, teses, dissertações e sites com conteúdo sobre anális= e de sentimentos e descoberta de conhecimento do cliente.  Foram consultadas as seguintes bases de dados: SciELO, IEEE Xplore, Scopus e Google Scholar= .

 

3.2 Estrutura d= a Base de Dados de Atendimento

 

A empresa varejista que forneceu s= eus dados para a realização deste trabalho possui um sistema interno com regist= ros de comentários de clientes sobre produtos e serviços, os quais são importad= os das mídias sociais e armazenados sem análise. A organização permitiu o uso = da base de dados, mas não autorizou a divulgação do seu nome e marca. Os dados utilizados neste trabalho foram anonimizados, ou seja, foram removidas informações pessoais que permitiriam a associação direta ou indireta a um indivíduo.

Para aprimorar produtos e serviços, diversos canais são utilizados para coletar feedback dos consumidores. O banc= o de dados inclui comentários obtidos por meio de mídias sociais, central de atendimento, lojas físicas, aplicativos e portais, todos registrados seguin= do a metodologia NPS. Esses comentários, que podem ser positivos, negativos ou neutros, muitas vezes contêm erros de ortografia e concordância, dificultan= do interpretação.

Na Figura 1, apresenta-se as categorias de d= ados e suas correlações que formam a base de atendimentos. Essas correlações foram estabelecidas com a ajuda de um especialista da empresa, por meio de reuniõ= es online e encontros presenciais, que foram fundamentais para compreender a estrutur= a da base de dados, resolver dúvidas, e discutir os resultados e as necessidades= da solução proposta.

 

Figura 1 – Estrutura da base de dados de atendimento

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Fonte: Autores (2024)

 

Assim, as correlações descritas a seguir têm relevância prática para a empresa, visando proporcionar um atendimento mais eficaz ao cliente com base no conhecimento obtido sobre os perfis específic= os.

As quatro principais correlações apontadas c= omo mais expressivas pelo especialista são as seguintes:

1) Correlação entre a Causa (comentário do cliente) X Perfil do Cliente, cruza= ndo os atributos sexo, idade, renda e estado.

2) Correlação entre a Causa (comentário do cliente) X Produto cartão, cruzando com os atributos faixa limite, tipo de produto, ciclo de vida e adicional.

3) Correlação entre a Causa (comentário do cliente) X Canais de atendimento, cruzando com= os atributos central de atendimento estandes/lojas, aplicativos, portais e = status da solicitação.

4) Correlação entre Causa (comentário de cliente) X Estratificação de notas detratoras do= NPS (notas de 0 a 6), segregadas em dois substratos: notas de 0 a 3 (detratores inferiores) e notas de 4 a 6 (detratores superiores).

Para conduzir os experimentos e validar a so= lução, foram apresentados apenas os resultados dos atributos da categoria perfil do cliente (sexo, idade, renda e estado) cruzados com NPS detratores. Embora outras categorias, como produto cartão e canais de atendimento, pudessem ser incluídas, isso não alteraria a conclusão de que a solução pode ser eficaz = para o objetivo proposto.

 

3.3  Metodologia Experimental

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A metodologia experimental foi dividida em cinco fases baseada no processo de Descoberta = de Conhecimento em Bases de Dados (Fayyad, Piatetsky-Shapiro e Smyth= , 1996): seleção da base de dados de atendimento, pré-processamento e transformação = dos dados, aplicação da RNC, comparação dos resultados experimentais com os indicadores detratores do NPS e interpretação e avaliação dos resultados:

Fase 1: S= eleção da base de dados de atendimento

A base de dados selecionada é do ano de 2021 com 17.= 547 registros e 59 atributos, contendo duas classes, masculino e feminino. = Os dados são oriundos, dos feedbacks de clientes sobre produtos e servi= ços, coletados pela central de atendimento, lojas físicas, aplicativos e portais, além dos resultados do NPS. A empresa não autorizou a divulgação do seu nom= e.

Fase 2: Pré-processamento e transformação dos dados

Inicialmente, a base foi extraída pelo siste= ma Quest Manager (QWST) e exportada para uma planilha MSExcel (.CSV). Utilizou-se o Google Colaboratory (Colab), uma ferramenta do = Google Research, para a leitura da base de dados. O Colab facilita a escrita e execução de código Python diretamente no navegador, sendo ideal para aprendizado de máquina e análise de dados. Foram instaladas e importadas no Colab as bibliotecas NLTK, Spacy, Pandas, Random, Numpy, Re, Seaborn, Matplotlib e PIL.

O pré-processamento da base incluiu a remoção de atributos, a limpeza, o tratamento dos textos, bem como a padronização dos tamanhos das sequências textuais. A remoção dos atributos = foi realizada após análise conduzida por especialistas da empresa que forneceu a base. Es= ses especialistas identificaram quais atributos não eram relevantes ou essencia= is para a análise, ou seja, aqueles que não contribuiriam para os resultados ou que poderiam gerar ruído nos dados. Essa avaliação permitiu a redução do conjunto de dados, mantendo apenas os atributos mais significativos.

Além disso, foi realizado um processo = de limpeza e transformação dos textos, que envolveu a remoção de caracteres especiais, de stopwords que são palavras irrelevantes para a análise, de números e de pontuações. Para permitir que = os textos fossem compreendidos pelas técnicas selecionadas, aplicou-se a tokenização, que consiste na segmentação das frases em palavras ou partes menores, e a vetorização de termos, que converte os textos em representações numéricas adequadas para processamento computacional.

Outro aspecto importante do pré-processamento foi a padronização do comprimento das sequências textuais, uma vez que os comentários possuíam tamanhos variados. Para resolver essa questão, utilizou-se a técnica de padding, que ajusta todas as sequências para um tamanho fixo, garantindo que as técnicas consigam processar os dados de maneira uniforme. Por fim, os textos foram transformados em vetores numéricos.

No caso das notas detratoras do NPS, focou-s= e nos comentários dos clientes da coluna ‘Causa’. Apenas os comentários associados aos detratores ou insatisfeitos que atribuíram notas de 0 a 6 no NPS foram selecionados para a realização dos experimentos computacionais. =

Vale destacar que todas as perguntas e respo= stas presentes na base de dados foram fornecidas pelos clientes diretamente atra= vés dos canais de comunicação da empresa, como a central de atendimento, estand= es ou lojas, aplicativos e portais.

O cálculo do NPS é feito da seguinte forma (= Baquero, 2022):

1. Coleta de Dados: pergunta-se aos clientes o quão p= rovável é que recomendem a empresa, usando uma escala de 0 a 10.<= /span>

2. Classificação: os clientes são divididos em três= grupos:

-Promotores (9-10): Altamente satisfeitos e propensos a recomendar.

-Neutros (7-8): Satisfeitos, mas não entusiásticos.

-Detratores (0-6): Insatisfeitos e podem prejudicar a reputação.

3. Cálculo do NPS: A fórmula é: NPS =3D Promotores – Detratores/Número total de respondente. O NPS varia de -100 a +100 e um val= or positivo indica lealdade dos clientes.

Por exemplo, em uma pesquisa com 50 pessoas,= 25 deram notas 9 e 10 (promotores), 20 deram notas 7 ou 8 (neutros) e 5 deram notas de 0 a 6 (detratores). Nesse sentido, o cálculo de NPS deve ser: 25 (promotores) – 5 (detratores) ÷ 50 (número total de pessoas que responderam= ) =3D 40%.

Destaca-se novamente que foram considerados = apenas as notas dos detratores (0 a 6) para a realização dos experimentos. As notas dos neutros (7 e 8) foram excluídas por serem comentários ambíguos, e as no= tas dos promotores (9 a 10) não foram incluídas porque se referem a comentários favoráveis sobre produtos e serviços da empresa. <= /p>

Após a remoção dos atributos em consenso com= o especialista da empresa e considerando somente as notas detratoras, a base = de dados utilizada nos experimentos ficou com 16 atributos e 17.547 registros.=

Apresenta-se na Tabela 1, os nomes e as desc= rições dos atributos.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Tabela 1 – Nomes e descrições dos atributos selecionados

Número do atributo

Nome do atributo

Descrição do atributo

1

NPS=

Notas de= 0 a 6 atribuídas pelos clientes

2

Perfil do NPS=

Detrator

3

Causa=

Comentár= io do cliente

4

Canal

Informações sobre o canal de atendimento real= izado por meio da central de atendimento, de estandes (lojas), de aplicativos ou portais

5 a 9

Cidade, Estado,

Sexo, Fa= ixa de idade, Faixa de renda <= /o:p>

Dados pessoais dos clientes

10<= /o:p>

Faixa limite<= /p>

Limite do cartão de crédito

11<= /o:p>

Adiciona= l

Cliente possui cartão de crédito ad= icionais

12<= /o:p>

Seguro

Cartão do cliente tem seguro oferecido pela empresa

13<= /o:p>

Ciclo de vida

Cartão fidelidade está ativo, inati= vo ou nunca ativo

14<= /o:p>

Fatura digital

Cliente ativo possui fatura digital

15<= /o:p>

Tipo de produto

Cartão é nacional ou internacional<= o:p>

16<= /o:p>

Status solicitação

Status do atendimento: automático, finalizado ou pendente

Fonte: Autores (2024)

 

Seguindo com o desenvolvimento das atividade= s das fases 1 e 2 foi necessário normalizar valores, ajustando-os para um interva= lo específico. O processo de normalização incluiu a remoção de 'stopwords'<= /i>, caracteres especiais, stemização, lematização, tokenização e padding de palavras. De acordo com Benhar e Alemán (2020), a extração eficiente de recursos textuais, como anotações, é essencial para aprimorar a precisão dos modelos de classificação.

Os autores destacam a importância de limpar e normalizar textos, removendo ruído e informações irrelevantes, e de utilizar tokenização e normalização para padronizar o formato do texto. Além disso, o artigo menciona a aplicação de técnicas de redução de dimensionalidade, com= o a vetorização de termos, para transformar textos em representações numéricas = mais adequadas para algoritmos de aprendizado de máquina.

Após essas etapas, os dados foram agrupados = em um único local para a aplicação dos modelos de análise. Foi criada a represent= ação bag of words com base no texto pré-processado.

Na sequência foram usadas ferramentas de classificação de texto para extrair conhecimento, começando pela identifica= ção de palavras-chave. A ferramenta Rake foi utilizada para criar um = ranking das principais palavras. Em seguida foram aplicadas as ferramentas Gensi= m e Bertopic para a representação de tópicos, além da medida TF-IDF (<= i>Term Frequency - Inverse Document Frequency).

Fase 3: A= plicação da RNC

O hardware utilizado foi um computado= r com placa de vídeo Graphics 620, processador Intel Core i7-8565U, SSD de 512GB, 16GB de memória e Windows 11 Home Single Language.=

Os experimentos foram realizados com Pyth= on 3.10.7 e Google Colaboratory, utilizando as bibliotecas Scikit-le= arn e spaCy para o processamento dos dados. A biblioteca TF-IDF (Term Frequency-Inverse Document Frequency) foi utilizada para avaliar a importância das palavras em documentos em relação a um corpus (Tietz et = al., 2017).

A base de da= dos pré-processada e transformada foi dividida, para a realização dos experimentos, em três partes: treinamento, teste e validação= . Os resultados da RNC foram comparados com duas técnicas de IA: a rede neural artificial do tipo Multilayer Perceptron (MLP)e a Árvore de Decisão, comumente aplicadas ao tipo de problema tratado neste trabalho. A escolha da técnica = para dar continuidade aos experimentos recaiu sobre aquela com  melhor desempenho, avaliado com base nas seguintes métricas: acurácia, precisão, recall e FI Score, descritas na Tabela 2.

 <= /o:p>

Tabela 2 - Métricas de Avaliação de Desempenho

Métrica

Descrição

Fórmula

Acurácia

Indica uma performance geral = do modelo. Dentre todas as classificações, quantas o modelo classificou corretamente;

Precisão

Dentre todas as classificações de cl= asse Positivo que o modelo fez, quantas estão corretas;

Recall<= /p>

Dentre todas as situações de classe Positivo como valor esperado, quantas estão corretas;

F1-Score

Média harmônica entre precisão e = recall.

Fonte: Adaptad= o de Provost e Fawcett (2013)

 

O desempenho também = foi avaliado pela Matriz de Confusão, que é uma tabela que permite obter as mét= ricas de avaliação de desempenho para das técnicas de IA, descritas acima. Aprese= nta-se na Tabela 3, a Matriz de Confusão.

 

Tabela 3 – Matriz de Confusão

Previsto

Real

       Sim

Não

Sim

Verdadeiro Positivo
(VP)

Falso Negativo
(FN)

Não

Falso Positivo
(FP)

Verdadeiro Negativo
(VN)

Fonte: Adaptado de = Provost e Fawcett (2013)

 <= /o:p>

Fase 4: Comparação dos resultados dos experimentos com os resultados dos indicadores detratores do NPS

A comparação= do NPS com os resultados dos experimentos foi realizada para saber se o NPS trouxe respostas de reclamações semelhantes às encontradas nos resultados dos experimentos com a RNC. A análise dos resultados comparativos foi realizada= em conjunto com o especialista da empresa varejista.

Fase 5: Interpretação e avaliação dos resultados

Os resultados obtidos com a aplicação da RNC foram avaliados com base nas correlações e cruzamentos de dados estabelecidos para a descobrir conhecimento sobre os clientes.

Apresenta-se= no Quadro 1, as correlações das categorias de dados utilizadas para os cruzamentos, com o objetivo de revelar insights sobre os clientes da empresa varejista.

 

Quadro 1 – Correlações de categorias de = dados para cruzamentos

 

 

Causa

(comentário do cliente)

Perfil do cliente (sexo, idade, r= enda e estado)

Produto cartão (faixa limite, tip= o de produto, ciclo de vida e adicional)

Canais de atendimento (central de atendimento, estandes/lojas, aplicativos, port= ais e status da solicitação)

Estratificação de notas do NPS (n= otas de 0 a 6, segregadas em dois substratos: notas de 0 a 3 (detratores inferiores) e notas de 4 a 6 (detratores superiores).

Fonte: Autores (2024)

 

4 APRESENTAÇÃO E DISCUSSÃO DOS RESULTADOS

 <= /o:p>

Nesta seção = os resultados dos experimentos computacionais são apresentados, analisados e discutidos, considerando as fases de desenvolvimento da metodologia experimental.

 <= /o:p>

4.1  Resultados dos experimentos computaciona= is

&nbs= p;

Fases 1 e= 2: Seleção da base de dados de atendimento, pré-processamento e transformação = dos dados

Após selecio= nar, pré-processar e transformar a base de dados, foram us= adas ferramentas de classificação de texto para extrair conhecimento, começando = pela identificação de palavras-chave. A ferramenta Rake<= /span> foi utilizada para criar um ranking das principais palavras, com as = dez primeiras listadas na Tabela 4.

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

 <= /o:p>

Tabela 4 – Ranking de palavras

Ranking

Palavras

Score

1<= /o:p>

atendim= ento / cartão / vencimento/ cancelamento

25.0

2<= /o:p>

cobrar/ juro/ cliente=

24.5

3<= /o:p>

cancela= mento / assinatura/ sistema

23.0

4<= /o:p>

atendimento / cartão / cobrar/ anuidade

16.8

5<= /o:p>

parcela= r / cartão / alimentação

15.8

6<= /o:p>

aumentar / limite / cancelar=

15.8

7<= /o:p>

limite / aumentar/ parcelar

15.5

8<= /o:p>

senha / cadastro / cartão

9.5

9<= /o:p>

diminuí= ram / atraso

9.5

10

bloquear / cartão

9.0

Fonte: Autores (2024)

A análise da= Tabela 4 revela as palavras-chave mais relevantes nos comentários dos clientes, com = base no score, que combina a frequência, o grau de concorrência com outras palavras e a relação grau/frequência. As palavras como atendimento, cartão, vencimento e cancelamento tiveram os maiores scores, sendo as mais citadas e frequentemente associadas a outras palavras importantes. A seguir, descreve-se a aplicação das ferramentas Gensim<= /span> e do Bertopic para a modelagem de tó= picos.

O texto foi = tokenizado e convertido em um dicionário de termos, atribuindo um índice a cada termo. Em seguida foi criada uma matriz de texto baseada nesse dicionário revelando tópicos como: cartão, limite, atendiment= o, compra e pagamento.

Os tópicos f= oram extraídos com a aplicação da medida TF-IDF (Term Frequency - Inverse Docume= nt Frequency). O TF-IDF é um cálculo estatístico utilizado como uma forma = de quantificar palavras em conjuntos de documentos. Em seguida, a aplicação do= Bertopic confirmou os cinco principais tópicos identificados pelo Gensim: cartão, limit= e, atendimento, pagamento e compra. Para validar a classificação de palavras-c= have e a modelagem de tópicos, a frequência das palavras foi calculada usando uma biblioteca de probabilidade, excluindo stopwords (Kit e Mojik, 2022)

As palavras = mais frequentes nos comentários dos clientes estão listadas na Tabela 5.

 <= /o:p>

Tabela 5 – Lista das palavras mais frequ= entes nos comentários

 

Palavras frequentes

 

1

cartão

4.931

2

limite

3.432

3

comprar<= /p>

2.115

4

atendimento

1.749

5

conseguir

1.656

6

pagar

1.650

7

nome empresa

1.499

8

aumentar

1.478

9

resolver=

1.416

10

baixo

1.322

Fonte: Autores (2024)

 <= /o:p>

A base de da= dos conta agora um novo atributo denominado Topic. Apresenta-se na Figura 2, a base de dados com o atributo Topic.

 <= /o:p>

 <= /o:p>

Figura 2 – Base de dados com o atributo = Topic

<= /p>

Fonte: Autores= (2024)

 

Fase 3 - = Aplicação da RNC na Análise de Sentimentos

Os testes determinaram que o melhor percentu= al de divisão da base de dados para aplicar as técnicas selecionadas foi de 50% para o treinamento, 30% p= ara teste e 20% validação. A melhor topologia para a RNC foi a seguinte: camada de entrada com 28 x 28 neurônios, camada de saída com 5 x 5 neurônio= s, duas camadas ocultas de 24 x 24 neurônios cada e o uso da função de ativaçã= o Softmax. O desempenho da RNC foi comparado com a MLP e com a Árvore de Decisão. Apresenta-se na Tabela 6, os resultados comparativos das técnicas aplicadas, considerando as métricas de avaliação de desempenho selecionadas.

 <= /o:p>

 Tabela 6 – Resultado do desempenho das técnicas selecionadas

Recall (F/M)

F1-score 

Rede Neural Convolucional= (RNC)

 

Apresenta-se na Figura 3, as = Matrizes de Confusão da RNC de acordo com a divisão da base em treinamento, teste e validação.

Figura 3 – Matrizes de Confusão da RNC

Fonte: Autores (2024)

 

Analisando-se os resultados da Tabela 6 e as matrizes da Figura 3, verificou-se que a RNC apresentou bom desempenho nas etapas de treinamento, teste e validação. Alé= m disso, foi a técnica que melhor classificou a classe “M”. Os resultados apresentar= am um ótimo equilíbrio entre precisão e recall, com uma baixa margem de erro. Apesar da presença de falsos negativos, o impacto desses erros foi reduzido devido à boa performance geral.

Desta forma, a RNC foi selecionada para a continuação dos experimentos. Os princi= pais motivos que levaram a essa decisão incluem:

  1. Melhor Acurácia:<= /b> Obteve a melhor acurácia em todos os conjuntos de dados.
  2. Equilíbrio entre Precisã= o e Recall: Conseguiu manter um equilíbrio mais satisfatório entre ambas as classe= s, garantindo que a classe M (Masculino) fosse melhor identificada do que= nas outras técnicas.
  3. Melhor Desempenho no = F1-Score: Demonstrou um desempenho mais consistente no F1-Score, com valo= res próximos para ambas as classes. Isso indica que o modelo não só identi= fica corretamente os exemplos positivos, mas também reduz a ocorrência de falsos positivos e falsos negativos.
  4. Análise da Matriz de Con= fusão: Revelou que a RNC teve a menor taxa de erros comparada aos outros mode= los. Embora ainda existam falsos negativos na classe M, seu desempenho foi consideravelmente melhor do que os modelos MLP e Árvore de Decisão.
  5. Capacidade de Capturar P= adrões Complexos:<= span style=3D'font-family:"Myriad Pro",sans-serif'> Redes Neurais são conhe= cidas por sua habilidade de aprender padrões complexos nos dados, permitindo maior flexibilidade e robustez na classificação. Isso pode justificar o melhor desempenho da RNC em relação às abordagens tradicionais como a = MLP e a Árvore de Decisão.

 <= /o:p>

Fase 4 – Comparação dos resultados dos experimentos com os resultados dos indicadores detratores do NPS

A partir dos= resultados auferidos nos experimentos foi realizada a comparação com os indicadores detratores do NPS. Esta atividade ocorreu em conjunto com o especialista da empresa, de modo a aproveitar sua expertise no fenômeno analisado.

Ao comparar = os tópicos identificados (cartão, limite, compras, atendimento, pagamentos e aumento) com os resultados do NPS expressos pelos clientes, foi possível estabelecer uma primeira correlação com as notas dos detratores (0 a 3).

Os tópicos q= ue geraram mais reclamações dos clientes foram relacionados ao cartão, limite e atendimento nos canais disponibilizados pela empresa, conforme ilustrado na= Figura 4, (A), que apresenta a comparação com os detratores do NPS de 0 a 3.

Por outro la= do, os tópicos com menor volume de comentários dos clientes foram: baixo, nome da empresa, comprar, não conseguir atendimento e resolver problema com a empresa. Vale destacar que o tópico "aumentar" não recebeu nenhum comentário dos clientes na correlação entre os experimentos analisados e os detratores do = NPS com notas de 0 a 3.

 

Figura 4 - Comparação dos resultados dos experimentos com os resultados do NPS

Fonte: Autores (2024)

 

Ao comparar = os resultados dos experimentos com as notas de NPS dos detratores, foi realiza= da uma segunda correlação, desta vez com as notas de 4 a 6.

Pode-se veri= ficar na Figura 4, (B) a comparação dos resultados com o NPS desses detratores. Os tópicos que mais geraram insatisfações entre os clientes foram relacionados= a cartão, limite e atendimento. Por outro lado, os tópicos que registraram me= nos reclamações foram: aumentar, baixo, comprar, pagar, nome da empresa e resol= ver problemas.

A análise mo= strou que muitos comentários dos clientes estavam relacionados a problemas com cartão, atendimento, limite, pagamento e questões não resolvidas pelos cana= is da empresa. Além disso, foi observado que alguns clientes expressaram insatisfação em relação a múltiplos problemas identificados pela solução desenvolvida, abordando dois ou até três dos principais tópicos em um único comentário.

 <= /o:p>

Fase 5: Interpretação e avaliação dos resultados

Após a class= ificação dos sentimentos pela RNC, foi realizada a correlação entre o atributo causa (comentário do cliente) e o atributo sexo em cruzamento com ‘idade’, ‘renda= ’, e ‘estado do cliente’. Os resultados obtidos foram organizados nos principais tópicos identificados como os mais relevantes para a análise: cartão, limit= e, compra, atendimento, pagamento e aumento. Essa abordagem permitiu identific= ar quais grupos de clientes demonstraram maior insatisfação e quais foram os principais problemas relatados. A saída da RNC consistiu em um conjunto de categorias (Tópicos) geradas com base na similaridade semântica dos comentários. Os resultados da correlação são apresentados na Figura 5.

 <= /o:p>

 <= /o:p>

Figura 5 - Identificação dos Tópicos Relevantes

 <= /o:p>

Fonte: Autores (2024)

O próximo = passo consistiu, juntamente com os especialistas da empresa, em aplicar as correlações e cru= zar os dados para obter insights a partir das reclamações dos clientes. = Nessa fase, foi correlacionado o comentário do cliente com sexo, idade, renda e estado do cliente. Os resultados são apresentados na Tabela 7, de acordo co= m os principais tópicos significativos para a

Tabela 7 – Correlação entre os atributos=

Cartão

A correlação entre os atributos 'causa' e 'sexo' revelou que a maioria dos clientes insatisfeitos com os serviços de cartão tem idade entre 36 e 40 anos e 51 e 60 anos, e renda mensal entre R$ 768,01 e R$ 1.625,00. O estado de São Paulo lidera com ce= rca de 30% das reclamações, tanto para perfis femininos quanto masculinos. Ou= tros estados, como Rio de Janeiro e Minas Gerais, tiveram aproximadamente 15% = das reclamações cada, enquanto os demais estados somaram cerca de 40%.= Limite=

Compra= r

Atendi= mento

Pagame= nto

Aument= o

Tópico

Conhecimento descoberto

Cartão=

Client= es com perfis diversos estão insatisfeitos com os serviços da empresa, principalmente devido à falta de aumento de limite, dificuldades no conta= to com a central de atendimento, problemas com desbloqueio e cancelamento de cartões, e dificuldade em obter informações sobre os cartões.<= /span>

Limite=

Existê= ncia de clientes com perfis diferentes com dificuldade em aumentar o limite do cartão, mesmo quando o cliente tem renda alta. <= /p>

Compra= s

Client= es com diversos problemas relacionados a compras efetuadas nas lojas própria= s da empresa, problemas com entregas, pois muitos produtos chegam danificados e com tempo de entrega atrasado em relação ao prometido pela empresa. =

Atendi= mento

Client= es reclamaram dos seguintes problemas: atendimento na central, lojas e aplicativos, pois há muita demora no tempo de espera para ser atendido. O= estado com mais reclamações no atendimento é o estado de São Paulo, provavelment= e em razão do volume de clientes.

Pagame= nto

Proble= mas relacionados a pagamento de fatura de cartão e multas altas para pagament= os efetuados em atraso.

Aument= o

Clientes de diferentes perfis, estados e idades estão insatisfeito com falta de aumento do limite de cré= dito e aumento de anuidade do cartão.

Fonte: Autores (= 2024)

 

Foi identifi= cado que clientes com renda mensal entre R$ 768,01 e R$ 1.625,00 e residentes em São Paulo estão insatisfeitos com questões relacionadas a cartões, limite, atendimento online e em lojas físicas. Entre os homens, 47% estão descontentes com parcelamento de compras, dificuldades no site da empresa e problemas ao usar o cartão nas lojas. Para as mulheres, 53% enfrentam dificuldades com análise de crédito, altas taxas para parcelamento e reembo= lso de compras.

Constatou-se= que 47% dos homens estão insatisfeitos com a demora e qualidade do atendimento, enquanto 48% das mulheres têm problemas com atendimento confuso e demorado. Quanto aos pagamentos, 52% das mulheres estão insatisfeitas com promoções incorretas, indisponibilidade da central de atendimento e altos custos, enquanto 48% dos homens reclamam de cobranças indevidas, problemas com o ap= p e atendimento ruim nas lojas físicas.

Foi constata= do também que a maioria dos clientes insatisfeitos com pagamentos e com dificuldades para resolver problemas com a empresa têm entre 51 e 60 anos. Entre os homens dessa faixa etária, 46% estão insatisfeitos com a falta de = ajuda pelo chat, problemas com o aplicativo e a central de atendimento. En= tre as mulheres, 49% relataram dificuldades no call= center, problemas para pagar a conta e suporte inadequado.

Além disso, = 51% dos homens estão descontentes com o aumento de limite de crédito e previdência, enquanto 49% das mulheres reclamam do aumento dos pontos de compra e limite= do cartão. Clientes com renda entre R$ 1.625,01 e R$ 2.705,00 também estão ins= atisfeitos com o limite de crédito e aumento de previdência, alegando que a empresa não ajusta os limites de forma adequada. São Paulo e Amazonas foram identificad= os como os estados com mais reclamações sobre o limite de crédito concedido pe= la empresa.

Os resultado= s dos experimentos foram comparados com os indicadores críticos do NPS detratores= . A correlação entre os comentários dos clientes e atributos como sexo, idade, renda e estado foi analisada em conjunto com os indicadores de NPS. Verificou-se que 60% dos clientes que deram as notas mais baixas no NPS (en= tre 0 e 3) são do sexo feminino, indicando uma alta insatisfação desse grupo co= m os serviços e produtos da empresa. Em contraste, 40% dos homens atribuíram not= as mais altas (entre 4 e 6), sugerindo que as mulheres tendem a expressar mais insatisfação nos canais de atendimento.

Constatou-se= que 70% dos clientes com renda entre R$ 768,01 e R$ 1.625,00 deram as piores notas = no NPS (entre 0 e 3), indicando insatisfação com o aumento de limite de crédit= o e previdência do cartão. Clientes com renda entre R$ 9.254,01 e R$ 20.888,00,= que representam 30% dos insatisfeitos, estão descontentes principalmente com o = cartão e o limite estabelecido. Entre os clientes com mais de 60 anos, 50% deram n= otas de 0 a 3, relatando problemas com o atendimento ao cliente, enquanto 50% dos clientes entre 18 e 50 anos deram notas de 4 a 6, expressando insatisfação = com o limite e anuidade do cartão. Esses resultados indicam que a empresa não ofereceu limites de cartão adequados para diferentes perfis de clientes, variando em renda, idade e sexo.

A análise mo= strou que 60% dos clientes insatisfeitos com NPS entre 0 e 3 são de São Paulo, evidenciando uma alta insatisfação com os serviços e produtos da empresa. Em comparação, apenas 40% dos clientes de outros estados deram notas entre 4 e= 6, com as principais reclamações relacionadas à falta de atendimento adequado = nas lojas e na central de atendimento.

A comparação= dos resultados dos experimentos com os NPS detratores (notas 0 a 3) revelou que= as principais queixas eram sobre limite e atendimento, enquanto os tópicos com menos comentários incluíram limite baixo, dificuldades em comprar, obter atendimento e resolver problemas. O tópico "aumentar" não recebeu comentários na faixa de NPS detrator entre 0 e 3. Os comentários dos client= es frequentemente abordam problemas com cartão, atendimento, limite e pagament= os, bem como questões não resolvidas pelos canais de atendimento da empresa. Ta= nto homens quanto mulheres mostraram insatisfação com vários problemas identificados.

Considera-se= que a aplicação da RNC na análise de sentimentos em comentários de clientes de uma empresa = do ramo varejista descobriu conhecimento nos comentários sobre as manifestaçõe= s de insatisfação dos clientes detratores, considerado relevante para apoiar a empresa na gestão deste cliente.

 

 

5 CONCLUSÃO

 <= /o:p>

A aplicação = da RNC buscou analisar os sentimentos expressados pelos clientes da empresa vareji= sta abordada neste estudo para apoiar a gestão do cliente. Para tanto foi realizada a avaliação e interpretação do conhecimento descoberto, além do cruzamento de= ste conhecimento produzido com os indicadores detratores do NPS.

Para atingir= os objetivos indicados foi considerada uma base de dados com registros provenientes da base de atendimento aos clientes, bem como os resultados do NPS. Os dados foram utilizados para a realização de experimentos computacio= nais baseados em métodos e técnicas voltados à análise e classificação de sentimentos, visando assim constituir uma solução eficiente e útil para ger= ir o cliente, considerando os produtos e serviços oferecidos.

A partir da = análise realizada, pode-se concluir que muitos comentários dos clientes dizem respe= ito a problemas com cartão, atendimento, limite, pagamento e problemas que não = são solucionados pelos canais de atendimento da empresa. Também foi possível ve= rificar que tanto o perfil feminino, quanto o perfil masculino demostram insatisfaç= ão com mais de um problema (tópicos identificados) indicado pela solução aplic= ada neste trabalho.

Assim, os re= sultados alcançados indicam que a solução desenvolvida é capaz de proporcionar descoberta de conhecimento do cliente que apoie a gestão dos clientes na empresa varejista, objeto de estudo neste trabalho.

Como contrib= uições deste trabalho para a Academia, indica-se a aplicação da RNC voltada à anál= ise de sentimentos de comentários de clientes como um tema de pesquisa atual e relevante. Isto porque as técnicas de análise de sentimentos de clientes denotam sua capacidade de produzir conhecimentos novos acerca do cliente e, portanto, merecem a atenção dos pesquisadores, que poderão aplicar a solução aqui desenvolvida em outros fenômenos e objetos de pesquisa.

Também se in= dica contribuições desta pesquisa para os profissionais, gestores e organizações= de mercado. Isto porque a solução desenvolvida mostrou-se relevante para a viabilização da aplicação de métodos e técnicas inteligentes em demandas e problemas reais das empresas atuais, no que concerne à descoberta de conhecimento a partir da análise de sentimentos em comentários realizados p= or clientes, podendo ser replicada em outros tipos de empresas (porte, setor de atuação, perfil de clientes).

Segundo Nonaka e Takeuchi (1997) = adquirir e descobrir o conhecimento do cliente é vital, principalmente para os negóc= ios, além de ajudar a empresa a aprender mais sobre o cliente que compra seus serviços e produtos. Portanto, ao realizar experimentos aplicando a RNC par= a a análise de sentimento do cliente e comparando com a metodologia NPS, prevê-= se importante contribuição para o campo científico da Inteligência Artificial e seus métodos e técnicas.

Segundo Baquero (2022), o NPS fornece uma visão clara da leal= dade dos clientes e do grau de satisfação, ajudando as empresas a entenderem o q= uão bem estão atendendo às expectativas dos clientes. Os resultados comprovam a importância do NPS quando comparado aos resultados dos experimentos computa= cionais realizados.

O desenho de pesquisa aqui delineado poderá permitir o desenvolvimento e validação de diferentes tipos de soluções inteligentes para a descoberta de conhecimento= do cliente, subsidiando assim uma melhor tomada de decisão dos gestores nas empresas acerca dos clientes.

Como limitaç= ões desta pesquisa indica-se a seleção da técnica inteligente inicialmente apli= cada para a realização dos experimentos realizados, que se restringiu à RNC. Essa técnica foi escolhida uma vez que trabalhos já publicados indicavam que essa técnica é aplicada para a análise de sentimentos. Outra limitação diz respe= ito a base de dados analisada. Não obstante sua relevância em função da diversi= dade e volume dos dados analisados, a base em questão retrata um fenômeno restri= to: a insatisfação de clientes em relação ao atendimento prestado por empresa varejista.

Há ainda a s= er mencionado o fato de que os experimentos foram realizados em uma única empr= esa, o que impossibilitou a comparação dos resultados com outros trabalhos da literatura. Embora a empresa abordada nesta pesquisa seja uma multinacional= do setor de varejo, há de se considerar as especificidades do negócio de atuaç= ão da empresa, bem como suas características únicas.

Não obstante, estima-se que em função do volume e características dos dados disponíveis na empresa em questão, os resultados dos experimentos ora realizados possam aj= udar a resolver problemas que ocorrem costumeiramente em empresas varejistas, não sendo possível, entretanto, generalizar os resultados para toda e qualquer empresa deste segmento.

Por fim, como indicação de pesquisas futuras, sugere-se realizar a aplicação desta soluçã= o em outras bases de dados, bem como em empresas de diferentes setores de atuaçã= o. Indica-se ainda a aplicação de outras técnicas inteligentes além da RNC, realizando-se assim um comparativo de suas métricas e resultados.

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Rede Neural Convolucional aplicada na Análise de Sentimentos em Comentários de Clientes de Empresa do=

Ramo Varejista

Maria Sheila Carneiro; Dacyr Dante de Oliveira Gatto; Renato= José Sassi; Marcos Antonio Gas= par

IS= SN 2237-4558  •<= /span>  Navus    Florianópolis    SC    v. 16 • p. 01-7jan./dez. 2025

7

 

 

 

                  =                                                                           <= /span>       

 

ISSN 2237-4558    Navus  • &n= bsp;Florianópolis  •  SC    v.9    n.2    <= /span>p. XX-XX    abr./jun. 2019

 

 

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