El endpoint de Análisis de Sentimiento devuelve una puntuación de sentimiento categorizada como Positiva, Neutral o Negativa, junto con una puntuación numérica, una relación y palabras clave asociadas con el sentimiento.

Los campos clave en la respuesta incluyen "tipo" (categoría de sentimiento), "puntuación" (puntuación numérica de sentimiento), "razón" (razón de sentimiento) y "palabras clave" (lista de palabras significativas con sus puntuaciones).

Los datos de respuesta están estructurados en formato JSON, conteniendo campos para tipo de sentimiento, puntuación, ratio, palabras clave, versión, autor, correo electrónico, código de resultado y mensaje de resultado.

El parámetro principal para el endpoint de Análisis de Sentimientos es la cadena de texto que deseas analizar en busca de sentimiento.

Los usuarios pueden utilizar los datos devueltos para categorizar contenido financiero, identificar tendencias en el sentimiento a lo largo del tiempo o mejorar la interacción del usuario al personalizar el contenido según el análisis de sentimiento.

Los casos de uso típicos incluyen analizar artículos de noticias para el sentimiento del mercado, evaluar la retroalimentación de los clientes sobre productos financieros y clasificar las publicaciones de blogs financieros según el sentimiento.

La precisión de los datos se mantiene a través de técnicas avanzadas de procesamiento del lenguaje y entrenamiento continuo del modelo en diversos textos financieros para asegurar una detección de sentimientos confiable.

Los usuarios pueden esperar patrones consistentes en las puntuaciones de sentimiento, con sentimientos positivos a menudo vinculados a noticias financieras favorables y sentimientos negativos asociados con recesiones o crisis.

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