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aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":itemsOrder":["title_v2","text"],":items":{"title_v2":{"id":"title-v2-8ccf885722","type":"heading3","lines":["Visão geral"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-811069da75","text":"\u003Cp\u003EA análise de sentimento é uma técnica usada para entender o tom ou o sentimento emocional (positivo, negativo ou neutro) de dados textuais. Ao utilizar técnicas avançadas, como processamento de linguagem natural (natural language processing, NLP) e aprendizado profundo, esse processo categoriza o sentimento como positivo, negativo ou neutro, oferecendo uma maneira estruturada de interpretar dados não estruturados, como avaliações de clientes, publicações em mídias sociais, tickets de suporte, ligações de vendas ou relatórios e pesquisas de mercado. Para as empresas, a análise de sentimento oferece uma vantagem estratégica. Por exemplo, com o aumento do volume de interações online com os clientes, essas ferramentas fornecem uma visão crucial da percepção da marca e do sentimento do cliente em escala. De modo semelhante, resumir as pesquisas de analistas pode ajudar a revelar o sentimento do mercado em relação a produtos, empresas ou tecnologias específicos. No entanto, uma análise de sentimento avançada requer mais do que uma estratégia. Este artigo explora a mecânica da análise de sentimento, seus vários tipos, desafios próprios, os benefícios oferecidos e os métodos de avaliar seu desempenho. Entender esses aspectos é fundamental para empresas que buscam transformar insights de sentimento em insights práticos que melhorem a experiência do cliente e refinem as estratégias de negócios.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}}},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"intro",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":itemsOrder":["title_v2","text"],":items":{"title_v2":{"id":"title-v2-e8b84a4bb1","additionalClasses":"headline-decoration","type":"heading3","lines":["Introdução à análise de sentimento"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-83b200d113","text":"\u003Cp\u003EA análise de sentimento é uma técnica essencial no campo de NLP que ajuda a determinar o tom emocional por trás de um texto. Ao categorizar o texto como positivo, negativo ou neutro, a análise de sentimento fornece às empresas insights críticos sobre as atitudes e as emoções dos clientes. Essas informações são indispensáveis para fundamentar decisões, melhorar produtos e serviços e aumentar a satisfação dos clientes.\u003C/p\u003E\n\u003Cp\u003ECom o crescimento exponencial das mídias sociais, as empresas recebem uma grande quantidade de feedback e comentários dos clientes. A análise de sentimento em mídias sociais permite que as empresas monitorem a percepção do público sobre sua marca, produtos ou campanhas em tempo real. Essa capacidade ajuda as empresas a identificar tendências, avaliar estratégias de marketing e lidar com o feedback negativo no tempo certo. O resultado? Maior engajamento do cliente, melhor reputação da marca e estratégias de negócios mais responsivas.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}}},"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"sentiment",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":itemsOrder":["title_v2","text_copy_860073138","title_v2_copy","text"],":items":{"title_v2":{"id":"title-v2-9a7422ba10","type":"heading3","lines":["Como funciona a análise de sentimento"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138":{"id":"text-f119f37e6b","text":"\u003Cp\u003EÉ possível realizar análise de sentimento por meio de dois fluxos de trabalho: usando algoritmos tradicionais baseados em aprendizado de máquina ou, atualmente, usando grandes modelos de linguagem (large language models, LLMs). \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy":{"id":"title-v2-2dbb10a3b2","type":"heading4","lines":["Usando algoritmos de aprendizado de máquina:"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-5c2d558cdd","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EA análise de sentimento requer várias etapas para analisar e determinar o sentimento expressado no texto. O processo começa com o \u003Cb\u003Epré-processamento de texto\u003C/b\u003E, que inclui a limpeza e a preparação do texto para análise. Esta etapa pode envolver remover pontuação, converter texto em letras minúsculas, remover palavras vazias e lidar com caracteres especiais (como emojis) para criar um conjunto de dados consistente.\u003C/p\u003E\n\u003Cp\u003EEm seguida, são aplicados \u003Cb\u003Ealgoritmos de classificação de sentimento\u003C/b\u003E para determinar o sentimento expresso no texto. Esses algoritmos usam técnicas como aprendizado de máquina, NLP e modelos estatísticos. As abordagens comuns incluem:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAlgoritmos baseados em regras\u003C/b\u003E que utilizam regras linguísticas predefinidas para determinar o sentimento.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAlgoritmos de aprendizado de máquina\u003C/b\u003E que aprendem com base em conjuntos de dados de treinamento rotulados para classificar o sentimento.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAlgoritmos baseados em léxico\u003C/b\u003E que dependem de dicionários de sentimento capazes de atribuir pontuações a palavras ou frases.\u003Cbr\u003E\n \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EA \u003Cb\u003Eclassificação da polaridade\u003C/b\u003E, um subconjunto de análise de sentimento, tem como foco a categorização de sentimentos expressos como positivos, negativos ou neutros. Essa forma de análise é fundamental para se chegar a decisões de negócios mais bem fundamentadas com base no feedback dos clientes, nas tendências das mídias sociais e nas pesquisas de mercado.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}}},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy_copy_c_1638553606":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_386496946":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_11499":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1886340130":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_820364889":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_11499_402096092":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_1149904237":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1179862951":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_1648528305":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_295276270":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1120714341":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"analysis",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":itemsOrder":["title_v2","text_copy_copy_295276270","title_v2_copy_copy","text_copy","title_v2_copy_copy_c","text_copy_copy","title_v2_copy_copy_c_1638553606","text_copy_copy_1648528305","title_v2_copy_copy_c_1120714341","text_copy_copy_820364889","title_v2_copy_copy_c_386496946","text_copy_copy_1149904237","title_v2_copy_copy_c_1886340130","text_copy_copy_11499","title_v2_copy_copy_c_1179862951","text_copy_copy_11499_402096092"],":items":{"title_v2":{"id":"title-v2-9b72bca232","type":"heading3","lines":["Ia generativa e análise de sentimento"],":type":"snowflake-site/components/title-v2"},"text_copy_copy_295276270":{"id":"text-4f1c81d659","text":"\u003Cp\u003EA análise de sentimento teve um grande avanço com a rápida evolução da IA generativa e da tecnologia de grandes modelos de linguagem (large language models, LLMs). Veja, a seguir, uma análise mais detalhada de como a IA generativa está redefinindo e aprimorando a análise de sentimento.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy":{"id":"title-v2-248ca896c0","type":"heading4","lines":["Melhor compreensão do contexto"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy":{"id":"text-352839546f","text":"\u003Cp\u003EModelos tradicionais dependem de técnicas de \"bag-of-words\" ou de estruturas simples de aprendizado profundo, que podem não reconhecer informações de sentimentos sutis. A IA generativa, reforçada por LLMs, se destaca em capturar nuances das emoções, sarcasmo e contexto mais profundo, resolvendo um desafio persistente na análise de sentimento.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-24301e12bf","type":"heading4","lines":["Aprendizado de ponta e ponta"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy":{"id":"text-d3ae462421","text":"\u003Cp\u003EA IA generativa reduz a dependência da referência dinâmica de grandes conjuntos de dados de treinamento por meio do uso de aprendizado “zero-shot” (zero amostra) ou “few-shot” (poucas amostras). Ela é capaz de analisar o sentimento em novas áreas, como finanças ou saúde, sem retrabalho extensivo, acelerando a implementação e a adaptabilidade.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1638553606":{"id":"title-v2-6bfc3f7ee9","type":"heading4","lines":["Análise multilíngüe e entre domínios"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_1648528305":{"id":"text-63d1556423","text":"\u003Cp\u003EDiferente dos sistemas tradicionais que requerem modelos distintos para diferentes linguagens, a IA generativa pode processar sentimentos em várias linguagens de forma contínua. Sua generalização em todos os setores reduz a necessidade de personalização específica de domínio, tornando-a altamente versátil.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1120714341":{"id":"title-v2-a0b49f2b9e","type":"heading4","lines":["Reconhecimento de emoção e intenção"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_820364889":{"id":"text-94f0aed423","text":"\u003Cp\u003EAlém da simples classificação de sentimentos (positivos, negativos, neutros), a IA generativa consegue identificar emoções complexas, como frustração, empolgação ou satisfação. Ela também pode detectar intenção no texto, como se uma mensagem transmite uma reclamação, elogio ou até sarcasmo, aumentando sua aplicabilidade no atendimento ao cliente.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_386496946":{"id":"title-v2-7ae5e858d3","type":"heading4","lines":["Melhoria e aumento (augmentation) de dados"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_1149904237":{"id":"text-e9dbc755fa","text":"\u003Cp\u003EA IA generativa pode gerar dados sintéticos rotulados para melhorar os modelos tradicionais de avaliação de sentimento, melhorando a precisão. Ela também pode limpar conjuntos de dados de sentimentos ruidosos corrigindo exemplos de marcação incorreta para melhorar a qualidade dos dados.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1886340130":{"id":"title-v2-d0b74b8611","type":"heading4","lines":["Análise de sentimento interativa e conversacional"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_11499":{"id":"text-4828463a75","text":"\u003Cp\u003EEmbora os classificadores tradicionais forneçam resultados estáticos, a IA generativa renova a interação em tempo real, explicando pontuações de sentimento e oferecendo contexto. Essa capacidade a torna indispensável em sistemas de IA conversacional, como chatbots ou fluxos de trabalho de suporte ao cliente.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1179862951":{"id":"title-v2-d168f3fc13","type":"heading4","lines":["Aprendizado personalizado e adaptável"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_11499_402096092":{"id":"text-f31dbf11cf","text":"\u003Cp\u003EAs empresas podem ajustar os modelos de IA generativa de acordo com os padrões específicos de percepção do setor. Ao integrar o feedback do usuário, esses modelos podem evoluir e se adaptar com o tempo, melhorando a precisão e a relevância com o uso.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}}},"container_1790538746":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_43816":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"types",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":itemsOrder":["title_v2","container_copy_43816"],":items":{"title_v2":{"id":"title-v2-dc9ebd2209","type":"heading3","lines":["Tipos de análise de sentimento"],":type":"snowflake-site/components/title-v2"},"container_copy_43816":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_29527":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_497870316":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_1007903673":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_558784833":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how-rag-works",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":itemsOrder":["text_copy_copy_29527","text_copy","flexible_column_cont","flexible_column_cont_497870316","flexible_column_cont_558784833","flexible_column_cont_1007903673"],":items":{"text_copy_copy_29527":{"id":"text-9a36a15e62","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EO tipo de análise de sentimento que uma organização escolhe depende dos objetivos que ela possui. O objetivo é revelar as preferências dos clientes? Coletar opiniões detalhadas sobre os produtos? Avaliar o impacto de um evento recente na reputação da marca? Ao analisar dados textuais, as empresas podem responder melhor ao feedback dos clientes, melhorar os produtos oferecidos e chegar a decisões fundamentadas. Veja, a seguir, três tipos mais conhecidos de análise de sentimento:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy":{"id":"text-7f99107e6e","additionalClasses":"list--blue-bullets","text":"\u003Cdiv\u003E\u003Cb\u003E \u003C/b\u003E\u003C/div\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EAnálise de sentimento no nível do documento:\u003C/b\u003E analisa o sentimento geral expresso em um documento ou texto, fornecendo uma compreensão de alto nível de se o texto é positivo, negativo ou neutro. Esse tipo de documento é útil para analisar avaliações de clientes, publicações em mídias sociais ou qualquer texto que transmita uma opinião.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAnálise de sentimento com base em aspectos:\u003C/b\u003E vai além da análise em nível de documento para entender o sentimento vinculado a componentes ou recursos específicos de um produto ou serviço. Por exemplo, um cliente pode elogiar a velocidade de um aplicativo e criticar sua interface de usuário. Esse nível de detalhes ajuda a neutralizar os fatores que levam à insatisfação do cliente, possibilitando melhorias mais inteligentes nos produtos.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAnálise de sentimento no nível da entidade:\u003C/b\u003E concentra-se em analisar o sentimento associado a entidades específicas, como marcas, produtos ou indivíduos. Ele revela como o assunto-alvo é percebido em tempo real. Ao monitorar as mudanças no sentimento, as organizações podem determinar o impacto das campanhas, capturar as mudanças na reputação da marca ou entender melhor o posicionamento competitivo no mercado.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cdiv\u003E\u003Cb\u003E \u003C/b\u003E\u003C/div\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"flexible_column_cont":{"id":"flexible-column-container-aac9f0b7da","type":"4-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-ac5d05d2eb",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":itemsOrder":[],":items":{}},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-b9ee1889b5",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":itemsOrder":["title_v2_copy_copy_c"],":items":{"title_v2_copy_copy_c":{"id":"title-v2-9dd6fa9207","type":"heading4","lines":["Análise 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