When you analyze your data with the new Tone Analyzer for Customer Engagement Endpoint, you’re analyzing it with an alternate Tone Analyzer model specifically trained on customer support conversations on Twitter. This model detects a new set of tones designed specifically for customer engagement use cases. The tones included are frustrated, sad, satisfied, excited, polite, impolite and sympathetic. Learn more about how the model was developed here.
With this addition, we now have two endpoints available:
1. Tone Analyzer General Purpose Endpoint
Use the Tone Analyzer General Purpose Endpoint to monitor social media and other web data. Analyze short-form text like tweets or reviews, or longer documents like articles and blog posts.
– Tones detected within the General Purpose Endpoint include joy, fear, sadness, anger, disgust, analytical, confident, tentative, openness, conscientiousness, extraversion, agreeableness, and emotional range.
2. Tone Analyzer Customer Engagement Endpoint (BETA)
Use the Tone Analyzer Customer Engagement Endpoint to monitor customer service & support conversations.
– Tones detected with this endpoint include frustrated, sad, satisfied, excited, polite, impolite and sympathetic
Imagine, you are in a conversation with a chatbot and you feel that the human angle is completely missing because the bot starts it's dialog with a usual "Hi" or "Hello". You may want to personify the conversation by adding the name of the person (who's logged in) to the boring "Hi" or "Hello". Ever thought of this? It's not just personification, How about wishing appropriately based on the time of the day someone invokes your chat application? Also, how about passing values back and forth during a conversation between the nodes or from application to a node?
Creating and maintaining chatbots in multiple languages can be costly, error-prone, and not easily scalable. Each change in a bot needs to be manually replicated across each language the bot supports. The manual nature of maintaining multilingual chatbots can have a real impact on continuous delivery as the language-specific changes must be performed by language experts and this takes time. Moreover, we need to pay for every single change – intents, entities, and bot output. This can get expensive very quickly — so, what's the best way to translate chatbots into different languages without impeding continuous delivery and disrupting DevOps?