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6.49927C28.04 6.17205 28.0174 5.66432 27.6902 5.35966V5.34837C27.363 5.055 26.8552 5.07755 26.5506 5.40477L18.7311 13.9577C18.686 14.0141 18.6296 14.0592 18.5731 14.1156C17.2981 15.3117 15.3009 15.2327 14.1162 13.9577L6.2854 5.39351C5.98075 5.05501 5.47299 5.03242 5.14577 5.33708C4.80727 5.64173 4.7847 6.1495 5.08935 6.47672L10.8327 12.7503L5.6084 17.4217C5.2699 17.7151 5.24733 18.2228 5.5407 18.5613C5.83407 18.8998 6.35311 18.9224 6.68033 18.629L11.9159 13.9464L12.9201 15.0409C13.8454 16.0564 15.0865 16.6093 16.418 16.6093C17.7495 16.6093 18.9906 16.0564 19.9159 15.0409L20.8637 14.0028L26.0428 18.6516H26.0654C26.4039 18.945 26.9117 18.9111 27.205 18.5726C27.4984 18.2341 27.4645 17.7264 27.126 17.433L21.9695 12.818L27.7466 6.49927Z\" fill=\"#249EDC\"/\u003E\r\n\u003C/svg\u003E\u003C/span\u003E\r\n\u003C/div\u003E","cssContent":".share-icon__svg *{pointer-events:none}.share-icon-group{display:flex;gap:24px;align-items:center}.share-icon{display:inline-block}.share-icon svg{height:32px}.share-icon__svg:hover path{fill:var(--ui-02);transition:300ms ease fill}.share-icon__svg path{transition:300ms ease fill}","jsContent":"const pageURL = window.location.href;\r\n\r\ndocument.addEventListener('click', (e) =\u003E {\r\n\r\nconst targetClassesArr = [...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"],":type":"snowflake-site/components/container"}},":itemsOrder":["container_949147658"],":type":"snowflake-site/components/container"},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/es/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment"}},":itemsOrder":["text","experiencefragment"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"id":"fundamentals-main-content","layout":"SIMPLE","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container":{"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"},"id":"overview","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-394cad0e6f","type":"heading3","lines":["Descripción general"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-74fca4101f","text":"\u003Cp\u003EEl análisis de sentimientos es una técnica que se utiliza para comprender el tono emocional o el sentimiento —positivo, negativo o neutro— de los datos textuales. Mediante el uso de técnicas avanzadas como el procesamiento del lenguaje natural (PLN) y el aprendizaje profundo, este proceso clasifica los sentimientos como positivos, negativos o neutros. Este análisis ofrece una forma estructurada de interpretar datos no estructurados como reseñas de clientes, publicaciones en redes sociales, tickets de asistencia, llamadas de ventas o estudios e informes del sector. El análisis de sentimientos ofrece una ventaja estratégica a las empresas que lo utilizan. Por ejemplo, ante un aumento del volumen de interacciones con los clientes en línea, estas herramientas ofrecen una perspectiva crucial sobre la percepción de la marca y la opinión de los clientes a escala. De forma similar, los resúmenes de las investigaciones de los analistas permiten conocer la opinión del sector sobre productos, empresas o tecnologías específicos. Sin embargo, para dominar el análisis de sentimientos hay que recurrir a varios enfoques.\u003C/p\u003E\r\n\u003Cp\u003EEn este artículo examinamos cómo funciona el análisis de sentimientos, qué tipos hay, cuáles son sus desafíos inherentes, qué ventajas ofrece y a qué métodos podemos recurrir para evaluar su rendimiento. Comprender estos aspectos es vital si las empresas quieren convertir la información sobre sentimientos en información práctica que mejore la experiencia del cliente y pueda perfeccionar las estrategias empresariales.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"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"},"id":"intro","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-9f720b4c22","additionalClasses":"headline-decoration","type":"heading3","lines":["Introducción al análisis de sentimientos"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-b00e91f0a6","text":"\u003Cp\u003EEl análisis de sentimientos es una técnica esencial en el campo del PLN que ayuda a determinar el tono emocional que hay detrás de un texto. Al clasificar el texto como positivo, negativo o neutro, el análisis de sentimientos proporciona a las empresas información crítica sobre las actitudes y emociones de los clientes. Esta información es muy valiosa para tomar decisiones fundamentadas, mejorar productos y servicios y aumentar la satisfacción del cliente.\u003C/p\u003E\n\u003Cp\u003EDebido al crecimiento exponencial de las redes sociales, las empresas reciben una gran cantidad de comentarios y opiniones de los clientes. El análisis de sentimientos aplicado a las redes sociales permite a las empresas supervisar la percepción pública de su marca, sus productos y sus campañas en tiempo real. Con ello, las empresas pueden identificar tendencias, evaluar las estrategias de marketing y abordar los comentarios negativos rápidamente. ¿El resultado? Mejora de la interacción con los clientes, fortalecimiento de la reputación de marca y estrategias empresariales más ágiles.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"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"},"id":"sentiment","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-d23d2f0f92","type":"heading3","lines":["Cómo funciona el análisis de sentimientos"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138":{"id":"text-a1746ebf7d","text":"\u003Cp\u003EEl análisis de sentimientos se puede realizar a través de dos flujos de trabajo: algoritmos tradicionales basados en aprendizaje automático o large language models (LLM). \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy":{"id":"title-v2-b6d19efbab","type":"heading4","lines":["Algoritmos de aprendizaje automático:"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-672518fd39","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl análisis de sentimientos comprende varios pasos para analizar y determinar el sentimiento expresado en el texto. El proceso comienza con el \u003Cb\u003Epreprocesamiento del texto\u003C/b\u003E, que incluye la limpieza y preparación del texto para su análisis. Este paso puede implicar la eliminación de la puntuación, la conversión del texto a minúsculas, la eliminación de palabras vacías y la gestión de caracteres especiales o emojis para crear un conjunto de datos coherente.\u003C/p\u003E\r\n\u003Cp\u003EA continuación, se aplican \u003Cb\u003Ealgoritmos de clasificación\u003C/b\u003E para clasificar el sentimiento expresado en el texto. Estos algoritmos recurren a técnicas como el aprendizaje automático, el PLN y los modelos estadísticos. Entre los enfoques más comunes se incluyen los siguientes:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAlgoritmos basados en reglas\u003C/b\u003E que utilizan reglas lingüísticas predefinidas para determinar el sentimiento\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAlgoritmos de aprendizaje automático\u003C/b\u003E que aprenden de conjuntos de datos de entrenamiento etiquetados para clasificar el sentimiento\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAlgoritmos basados en léxico\u003C/b\u003E que se basan en diccionarios de sentimientos que asignan puntuaciones a palabras o frases\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELa \u003Cb\u003Eclasificación de polaridad\u003C/b\u003E se trata de un subconjunto del análisis de sentimientos que se centra en clasificar los sentimientos expresados como positivos, negativos o neutros. Este tipo de análisis es fundamental para tomar decisiones empresariales mejor informadas basadas en los comentarios de los clientes, las tendencias en las redes sociales y los estudios de mercado.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138","title_v2_copy","text"],":type":"snowflake-site/components/container"},"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"},"id":"analysis","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-63a479f3b9","type":"heading3","lines":["Análisis de sentimientos e IA generativa"],":type":"snowflake-site/components/title-v2"},"text_copy_copy_295276270":{"id":"text-959039f51c","text":"\u003Cp\u003EEl análisis de sentimientos ha dado un gran salto adelante con la rápida evolución de la IA generativa basada en LLM. Veamos con más detalle cómo la IA generativa está redefiniendo e impulsando el análisis de sentimientos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy":{"id":"title-v2-b1c9b588e3","type":"heading4","lines":["Mayor comprensión del contexto"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy":{"id":"text-73e0357fcb","text":"\u003Cp\u003ELos modelos tradicionales se basan en técnicas de “bolsa de palabras” o en marcos sencillos de aprendizaje profundo, que pueden pasar por alto indicios sutiles de sentimiento. La IA generativa, impulsada por los LLM, destaca por su capacidad para captar matices en las emociones, sarcasmo e información contextual más profunda, con lo que resuelve un desafío persistente en el análisis de sentimientos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-4e9da3b6ef","type":"heading4","lines":["Aprendizaje zero-shot y few-shot"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy":{"id":"text-599b8341ac","text":"\u003Cp\u003ELa IA generativa reduce la dependencia de la referencia dinámica a grandes conjuntos de datos de entrenamiento mediante el uso de aprendizaje zero-shot y few-shot. Puede analizar los sentimientos en nuevos ámbitos, como las finanzas o la sanidad, sin necesidad de un reentrenamiento exhaustivo, lo que acelera su implementación y adaptabilidad.\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-bf0bfe5448","type":"heading4","lines":["Análisis multilingüe e interdisciplinar"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_1648528305":{"id":"text-1dec3ba835","text":"\u003Cp\u003EA diferencia de los sistemas heredados, que requieren modelos independientes para cada idioma, la IA generativa puede procesar el sentimiento en varios idiomas. Su capacidad para generalizar en todos los sectores reduce la necesidad de personalización específica para cada ámbito, lo que la hace muy 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-v2-87d57f3eae","type":"heading4","lines":["Reconocimiento de emociones e intenciones"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_820364889":{"id":"text-fe3d0a8bbc","text":"\u003Cp\u003ELa IA generativa va más allá de la simple clasificación de sentimientos (positivos, negativos, neutros), e identifica emociones complejas como la frustración, la emoción o la satisfacción. También puede detectar intenciones en el texto, por ejemplo, si un mensaje transmite una queja o un elogio, o incluso si es sarcástico, lo que aumenta su aplicabilidad en el servicio de atención al 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-a97b417a1c","type":"heading4","lines":["Mejora y enriquecimiento de datos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_1149904237":{"id":"text-ea8b77bbf4","text":"\u003Cp\u003ELa IA generativa puede generar datos etiquetados sintéticos para mejorar los modelos de sentimientos tradicionales y aumentar su precisión. Además, puede limpiar conjuntos de datos de sentimientos desordenados y corregir ejemplos mal etiquetados para mejorar la calidad de los datos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1886340130":{"id":"title-v2-0f90e607ec","type":"heading4","lines":["Análisis de sentimientos interactivo y conversacional"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_11499":{"id":"text-1996ebee6b","text":"\u003Cp\u003EMientras que los sistemas de clasificación tradicionales proporcionan resultados estáticos, la IA generativa posibilita la interacción en tiempo real al explicar las puntuaciones de sentimiento y ofrecer contexto. Esta capacidad la hace especialmente útil en sistemas de IA conversacional, como los chatbots o los flujos de trabajo de asistencia al 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-v2-8de6d9db89","type":"heading4","lines":["Aprendizaje personalizable y adaptativo"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_11499_402096092":{"id":"text-52b3a211df","text":"\u003Cp\u003ELas empresas pueden ajustar los modelos de IA generativa para adaptarlos a patrones de sentimientos específicos de su sector. Al integrar los comentarios de los usuarios, estos modelos pueden evolucionar y adaptarse con el tiempo, y mejorar su precisión y relevancia con el uso.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":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"],":type":"snowflake-site/components/container"},"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"},"id":"types","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2":{"id":"title-v2-0385d37bf0","type":"heading3","lines":["Tipos de análisis de sentimientos"],":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"},"id":"how-rag-works","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"text_copy_copy_29527":{"id":"text-7acf65841d","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl tipo de análisis de sentimientos que elija una organización dependerá de sus objetivos. ¿El objetivo es descubrir las preferencias de los clientes? ¿Se quieren identificar opiniones detalladas sobre los productos? ¿Se pretende calcular el impacto de un evento reciente en la reputación de la marca? Mediante el análisis de datos textuales, las empresas pueden responder mejor a los comentarios de los clientes, optimizar sus ofertas y tomar decisiones informadas. A continuación se presentan tres tipos populares de análisis de sentimientos:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy":{"id":"text-1612c06038","additionalClasses":"list--blue-bullets","text":"\u003Cdiv\u003E\u003Cb\u003E \u003C/b\u003E\u003C/div\u003E\n\u003Cul\u003E\n\u003Cli\u003EEl \u003Cb\u003Eanálisis de sentimientos a nivel de documento\u003C/b\u003E examina el sentimiento general expresado en un texto o documento, y ofrece una visión global de si el contenido es positivo, negativo o neutro. Este tipo de análisis es útil para analizar reseñas de clientes, publicaciones en redes sociales o cualquier texto que transmita una opinión.\u003C/li\u003E\n\u003Cli\u003EEl \u003Cb\u003Eanálisis de sentimientos basado en aspectos\u003C/b\u003E va más allá del análisis a nivel de documento, ya que busca identificar el sentimiento asociado a componentes o características específicas de un producto o servicio. Por ejemplo, un cliente puede elogiar la velocidad de una aplicación, pero criticar su interfaz de usuario. Este nivel de detalle ayuda a las empresas a centrarse en lo que provoca la insatisfacción de los clientes para aplicar mejoras más inteligentes a los productos.\u003C/li\u003E\n\u003Cli\u003EEl \u003Cb\u003Eanálisis de sentimientos a nivel de entidad\u003C/b\u003E se centra en analizar el sentimiento asociado a entidades específicas, como marcas, productos o personas. Revela cómo se percibe a un sujeto objetivo en tiempo real. Al hacer un seguimiento de los cambios en los sentimientos, las organizaciones pueden determinar el impacto de las campañas, captar los cambios en la reputación de la marca o comprender mejor el posicionamiento competitivo en el 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-9a759198f1","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":{"id":"container-c380bbf2ff","layout":"SIMPLE",":items":{},":itemsOrder":[],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"id":"container-d4384214a3","layout":"SIMPLE",":items":{"title_v2_copy_copy_c":{"id":"title-v2-aa8583a25d","type":"heading4","lines":["Análisis de sentimientos a nivel de 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análisis"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2_copy_copy_c"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"id":"container-5e99103221","layout":"SIMPLE",":items":{"text_copy_copy_82036":{"id":"text-74fd6f118e","text":"\u003Cp\u003EEvalúa el sentimiento general de un documento o texto completo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_3":{"id":"container-30a7d4abf2","layout":"SIMPLE",":items":{"text_copy_copy_82036":{"id":"text-8588adcc51","text":"\u003Cp\u003EEvalúa los sentimientos relacionados con aspectos o características específicos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_4":{"id":"container-3b54aba2bb","layout":"SIMPLE",":items":{"text_copy_copy_82036":{"id":"text-db54b406e6","text":"\u003Cp\u003EAnaliza los sentimientos vinculados a entidades concretas como marcas o productos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false},"flexible_column_cont_558784833":{"id":"flexible-column-container-a2c2fdb91c","type":"4-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-5e5452192f","layout":"SIMPLE",":items":{"title_v2_copy_copy_c":{"id":"title-v2-f4088151c0","type":"heading4","lines":["Caso de uso"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2_copy_copy_c"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"id":"container-a03a9ad5aa","layout":"SIMPLE",":items":{"text_copy_copy_82036":{"id":"text-2fe9b2b1bd","text":"\u003Cp\u003EIdeal para resumir las reseñas de los clientes o los sentimientos en las redes sociales a nivel general.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_3":{"id":"container-2684255099","layout":"SIMPLE",":items":{"text_copy_copy_82036":{"id":"text-3ba2574737","text":"\u003Cp\u003EAyuda a las empresas a comprender qué características del producto generan satisfacción o quejas.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_4":{"id":"container-423b3f03b3","layout":"SIMPLE",":items":{"text_copy_copy_82036":{"id":"text-d66f842ca0","text":"\u003Cp\u003EHace un seguimiento de la reputación de la marca, el impacto de las campañas y el sentimiento competitivo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false},"flexible_column_cont_1007903673":{"id":"flexible-column-container-e9546cc759","type":"4-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-df884efce2","layout":"SIMPLE",":items":{"title_v2_copy_copy_c":{"id":"title-v2-705d43fbaf","type":"heading4","lines":["Nivel de detalle"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2_copy_copy_c"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"id":"container-e3600108dd","layout":"SIMPLE",":items":{"text_copy_copy_82036":{"id":"text-7934508f1e","text":"\u003Cp\u003EProporciona una clasificación general del sentimiento (positivo, negativo, neutro).\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_3":{"id":"container-41fc59237b","layout":"SIMPLE",":items":{"text_copy_copy_82036":{"id":"text-17f50ddda1","text":"\u003Cp\u003EOfrece información detallada sobre los sentimientos por aspecto (por ejemplo, velocidad del producto frente a usabilidad).\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_4":{"id":"container-df81527f26","layout":"SIMPLE",":items":{"text_copy_copy_82036":{"id":"text-ec530475b4","text":"\u003Cp\u003ESe centra en los cambios de sentimiento relacionados con entidades individuales.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false}},":itemsOrder":["text_copy_copy_29527","text_copy","flexible_column_cont","flexible_column_cont_497870316","flexible_column_cont_558784833","flexible_column_cont_1007903673"],":type":"snowflake-site/components/container"}},":itemsOrder":["title_v2","container_copy_43816"],":type":"snowflake-site/components/container"},"container_copy_copy__1158523250":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138":"aem-GridColumn aem-GridColumn--default--12"},"id":"challenges","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-49e623d6db","type":"heading3","lines":["Desafíos en el análisis de sentimientos"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138":{"id":"text-c024c7d6b8","text":"\u003Cp\u003EEl análisis de sentimientos tiene un enorme potencial para obtener información sobre los clientes y el mercado, pero es necesario abordar algunas cuestiones fundamentales para obtener resultados precisos y fiables.\u003C/p\u003E\n\u003Cp\u003EUno de los principales desafíos radica en la capacidad para detectar el sarcasmo y la ironía. Los sistemas automatizados a menudo interpretan erróneamente estas formas de expresión, lo que da lugar a resultados de análisis de sentimientos imprecisos. Para solventar esta carencia, se necesitan técnicas avanzadas de PLN que permitan detectar y comprender las complejidades más sutiles del tono y la intención en el texto.\u003C/p\u003E\n\u003Cp\u003EOtro reto es el de poder gestionar la negación y las dependencias contextuales. Palabras como “no” o “ninguno” pueden invertir el sentimiento de una oración. Los algoritmos sofisticados pueden interpretar la estructura de las oraciones en su contexto. Sin esta capacidad, los algoritmos de análisis de sentimientos pueden equivocarse al clasificar las afirmaciones negativas, lo que podría dar lugar a conclusiones erróneas.\u003C/p\u003E\n\u003Cp\u003EEl lenguaje y los matices culturales complican aún más el análisis de sentimientos. Cada idioma presenta características lingüísticas, expresiones idiomáticas y contextos culturales únicos que afectan al análisis de sentimientos. Por ejemplo, ciertas palabras o frases pueden tener diferentes connotaciones o implicaciones emocionales en diferentes culturas. Los LLM están demostrando ser una excelente solución para el análisis de sentimientos, y es que, al entrenarse con grandes volúmenes de textos diversos, son muy hábiles a la hora de detectar el sarcasmo y la ironía, y reconocer señales contextuales, el tono y los patrones lingüísticos propios de estos recursos expresivos; algo que a los modelos tradicionales les cuesta especialmente. Para abordar este problema, los modelos de sentimientos necesitan un entrenamiento exhaustivo sobre cánones multilingües y conjuntos de datos con información cultural.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138"],":type":"snowflake-site/components/container"},"container_copy_copy_445119867":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"id":"related","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"flexible_column_cont":{"id":"flexible-column-container-feb35c4d95","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-6ff671431c","layout":"SIMPLE","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"card_v2":{"id":"card-v2-c21f97fab4","configurationStatus":{"configured":true,"message":""},"layoutStyle":"vertical","title":{"id":"title","type":"heading4","lines":["Análisis de sentimientos con Snowflake Cortex y tablas de Iceberg"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.youtube.com/watch?v=Roko9aqtzl8"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Ver el vídeo"},"image":{"id":"image","alt":"Sample alt text","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8ae04ca5-62df-4e11-9224-59b9cb7dcf47/snowflake%25403x.png?quality=85&preferwebp=true","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"id":"container-4b4b984747","layout":"SIMPLE","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"card_v2_copy":{"id":"card-v2-c1594f28c7","configurationStatus":{"configured":true,"message":""},"layoutStyle":"vertical","title":{"id":"title","type":"heading4","lines":["Obtén información práctica a partir de datos no estructurados con Snowflake Cortex AI "],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.youtube.com/watch?v=r6gVZ5m-K8c "},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Ver el vídeo"},"image":{"id":"image","alt":"Sample alt text","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8ae04ca5-62df-4e11-9224-59b9cb7dcf47/snowflake%25403x.png?quality=85&preferwebp=true","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2_copy"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false}},":itemsOrder":["flexible_column_cont"],":type":"snowflake-site/components/container"},"container_copy_copy__683185918":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_copy_11499":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1714779250":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_11499_446974872":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"id":"benefits","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-a8e63a7d60","type":"heading3","lines":["¿Cuáles son las ventajas del análisis de sentimientos?"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138":{"id":"text-78fbdcb931","text":"\u003Cp\u003EEl análisis de sentimientos revela información fundamental sobre las opiniones y los comentarios de los clientes, lo que proporciona a las empresas una ventaja estratégica a la hora de comprender las percepciones del mercado y responder a ellas. Mediante el análisis de sentimientos, las empresas pueden extraer información útil para tomar decisiones inmediatas y estratégicas a largo plazo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-fc5fa307a2","type":"heading4","lines":["Mejora de la reputación de la marca y la fidelidad de los clientes"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_11499":{"id":"text-b4ad59703d","text":"\u003Cp\u003EUna ventaja clave del análisis de sentimientos es su capacidad para reforzar la reputación de la marca y la satisfacción de los clientes. Al identificar tanto los comentarios positivos como las experiencias negativas en la opinión de los clientes, las organizaciones pueden abordar los problemas a tiempo y evitar que se conviertan en asuntos graves. Esta capacidad no solo impulsa mejoras en los productos y servicios, sino que también fomenta relaciones más sólidas con los clientes, lo que en última instancia conduce a una mayor satisfacción y fidelidad por su parte.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1714779250":{"id":"title-v2-5b937b32d1","type":"heading4","lines":["Información detallada sobre el mercado y la competencia"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_11499_446974872":{"id":"text-b8db1bbb90","text":"\u003Cp\u003EAdemás de favorecer la comprensión de los comentarios de los clientes, el análisis de sentimientos ayuda a las empresas a identificar tendencias en el mercado y a realizar análisis de la competencia. 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