1. A computer-implemented method, comprising: receiving, by a processor, interaction data corresponding to one or more interactions between a customer and a customer support representative and storing said interaction data in a memory; extracting the stored interaction data from the memory and detecting, by the processor, the presence of at least one language associated with the interaction data by comparing whole text strings or portions of text in the interaction data with available language detection models for different languages, and predicting a best matching language corresponding to the interaction data; generating, by the processor, textual content in a plurality of languages corresponding to the interaction data based at least in part on translating the interaction data using two or more languages different than the at least one language; determining, by the processor, at least one emotion score for text corresponding to each language from among the plurality of languages; determining, by the processor, an aggregate emotion score using the at least one emotion score for the text corresponding to the each language; modeling, by the processor, an interaction experience of the customer based at least in part on the aggregate emotion score; and providing at least one recommendation to the customer based on said modeled interaction experience.
1. A computer-implemented method, comprising: receiving, by a processor, interaction data corresponding to one or more interactions between a customer and a customer support representative and storing said interaction data in a memory; extracting the stored interaction data from the memory and detecting, by the processor, the presence of at least one language associated with the interaction data by comparing whole text strings or portions of text in the interaction data with available language detection models for different languages, and predicting a best matching language corresponding to the interaction data; generating, by the processor, textual content in a plurality of languages corresponding to the interaction data based at least in part on translating the interaction data using two or more languages different than the at least one language; determining, by the processor, at least one emotion score for text corresponding to each language from among the plurality of languages; determining, by the processor, an aggregate emotion score using the at least one emotion score for the text corresponding to the each language; modeling, by the processor, an interaction experience of the customer based at least in part on the aggregate emotion score; and providing at least one recommendation to the customer based on said modeled interaction experience. 8. The method of claim 1 , wherein the aggregate emotion score is determined by performing one of averaging or weighted averaging of the at least one emotion score.