El endpoint de Reconocimiento devuelve un objeto JSON que contiene información emocional derivada del texto de entrada. Esto incluye una puntuación de confianza, emociones detectadas, sentimiento general, puntuación de sentimiento, subjetividad y un resumen del análisis.

Los campos clave en la respuesta incluyen "confidence_score" (precisión del análisis), "emotions" (estados emocionales detectados), "overall_sentiment" (clasificación del sentimiento general), "sentiment_score" (valor numérico del sentimiento) y "summary" (resumen conciso de los hallazgos).

El endpoint de Reconocimiento acepta parámetros como "texto" (el texto de entrada para el análisis), "verificación ortográfica" (para habilitar o deshabilitar la verificación ortográfica) y "palabras clave" (para incluir la extracción de palabras clave en el análisis).

Los datos de la respuesta están estructurados como un objeto JSON con campos anidados. El objeto principal contiene métricas generales como confianza y sentimiento, mientras que el campo "emociones" enumera puntajes emocionales específicos, lo que permite una interpretación fácil de los resultados.

La API analiza texto de varias fuentes, incluidas publicaciones en redes sociales, consultas de soporte al cliente, encuestas y reseñas. Esta diversa entrada de datos ayuda a garantizar una comprensión completa del sentimiento emocional en diferentes contextos.

Los casos de uso típicos incluyen monitorear el sentimiento de la marca en las redes sociales, mejorar el servicio al cliente al detectar estados emocionales, analizar la retroalimentación del producto para mejoras y realizar investigaciones de mercado para comprender las emociones de la audiencia.

Los usuarios pueden aprovechar los datos devueltos para adaptar las estrategias de comunicación, identificar áreas que necesitan mejora y responder a las emociones de los clientes en tiempo real. Por ejemplo, un puntaje de sentimiento bajo puede provocar una acción inmediata para abordar las preocupaciones de los clientes.

La precisión de los datos se mantiene a través de técnicas avanzadas de procesamiento de lenguaje natural y entrenamiento continuo del modelo en conjuntos de datos diversos. Actualizaciones regulares y controles de calidad aseguran que la API se adapte al uso del lenguaje en evolución y la expresión emocional.

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