System and method for actionizing comments using voice data
A system and method for processing and actionizing structured and unstructured experience data is disclosed herein. In some embodiments, a system may include a natural language processing (NLP) engine configured to transform a data set into a plurality of concepts within a plurality of distinct contexts, and a data mining engine configured to process the relationships of the concepts and to identify associations and correlations in the data set. In some embodiments, the method may include the steps of receiving a data set, scanning the data set with an NLP engine to identify a plurality of concepts within a plurality of distinct contexts, and identifying patterns in the relationships between the plurality of concepts. In some embodiments, the data set may include voice data from a voice based assistant or a voice based survey.
1. A system for processing and actionizing experience data, the system comprising:
a server comprising a natural language processing (NLP) engine; and
a relational database;
wherein a plurality of communications is received at the server, each of the plurality of communications comprises voice data, wherein the voice data comprises structured or unstructured data, and wherein the voice data comprises lexical data transcribed from speech data;
wherein the voice data from each of the plurality of communications is parsed for individual phrases to generate a plurality of phrases;
wherein one or more phrases are selected from the plurality of phrases based on a predetermined parameter;
wherein the NLP engine is to predict one or more annotations for the one or more phrases based upon a score, wherein to predict the one or more annotations for the one or more phrases based upon the score comprises to (i) predict one or more annotations for the one or more phrases based upon a machine learning score, (ii) determine whether the machine learning score is less than a predetermined threshold score, and (iii) predict the one or more annotations for the one or more phrases based upon a reference score in response to a determination that the machine learning score is less than the predetermined threshold score; and
wherein the one or more annotations are stored at the relational database in response to prediction of the one or more annotations.
2. The system of claim 1 , wherein the voice data is collected from a voice assistant.
3. The system of claim 1 , wherein the voice data is collected from a voice-based survey.
4. The system of claim 1 , further comprising a transcription engine, wherein the speech data is transcribed by the transcription engine to generate the voice data.
5. The system of claim 1 , wherein the server is to generate a dashboard web page for a user that includes the one or more annotations in response to prediction of the one or more annotations.
6. The system of claim 1 , wherein the one or more annotations comprise a sentiment, a theme, or any named entity of the one or more phrases.
7. The system of claim 6 , wherein the one or more annotations are stored at the relational database in an annotation table that includes one or more records in response to prediction of the one or more annotations, wherein each record corresponds to an annotation, and wherein each record includes the sentiment, the named entity, a primary tag indicative of a subject matter, or a secondary tag indicative of the theme.
8. A method for processing and actionizing experience data, the method comprising:
receiving, by a server, a plurality of communications, wherein each of the plurality of communications comprises voice data, wherein the voice data comprises structured or unstructured data, and wherein the voice data comprises lexical data transcribed from speech data;
parsing, by the server, the voice data from each of the plurality of communications for individual phrases to generate a plurality of phrases;
selecting, by the server, one or more phrases from the plurality of phrases based on a predetermined parameter in response to parsing the voice data;
predicting, by the server with a natural language processing (NLP) engine, one or more annotations for the one or more phrases based upon a score, wherein predicting the one or more annotations for the one or more phrases based upon the score comprises (i) predicting one or more annotations for the one or more phrases based upon a machine learning score, (ii) determining whether the machine learning score is less than a predetermined threshold score, and (iii) predicting the one or more annotations for the one or more phrases based upon a reference score in response to determining that the machine learning score is less than the predetermined threshold score; and
storing, by the server, the one or more annotations at a relational database in response to predicting the one or more annotations.
9. The method of claim 8 , further comprising collecting the voice data from a voice assistant, wherein receiving the plurality of communications comprises receiving the plurality of communications in response to collecting the voice data.
10. The method of claim 8 , further comprising collecting the voice data from a voice-based survey, wherein receiving the plurality of communications comprises receiving the plurality of communications in response to collecting the voice data.
11. The method of claim 8 , further comprising transcribing, by a transcription engine, the speech data to generate the voice data, wherein receiving the plurality of communications comprises receiving the plurality of communications in response to transcribing the speech data.
12. The method of claim 8 , further comprising generating, by the server, a dashboard web page for a user that includes the one or more annotations in response to predicting the one or more annotations.
13. The method of claim 8 , wherein the one or more annotations comprise a sentiment, a theme, or any named entity of the one or more phrases.
14. The method of claim 13 , wherein storing the one or more annotations at the relational database comprises storing the one or more annotations in an annotation table that includes one or more records in response to predicting the one or more annotations, wherein each record corresponds to an annotation, and wherein each record includes the sentiment, the named entity, a primary tag indicative of a subject matter, or a secondary tag indicative of the theme.
15. One or more non-transitory, computer-readable storage media comprising a plurality of instructions stored thereon that, in response to being executed, cause a server to:
receive a plurality of communications, wherein each of the plurality of communications comprises voice data, wherein the voice data comprises structured or unstructured data, and wherein the voice data comprises lexical data transcribed from speech data;
parse the voice data from each of the plurality of communications for individual phrases to generate a plurality of phrases;
select or more phrases from the plurality of phrases based on a predetermined parameter in response to parsing the voice data;
predict, with a natural language processing (NLP) engine, one or more annotations for the one or more phrases based upon a score, wherein to predict the one or more annotations for the one or more phrases based upon the score comprises to (i) predict one or more annotations for the one or more phrases based upon a machine learning score, (ii) determine whether the machine learning score is less than a predetermined threshold score, and (iii) predict the one or more annotations for the one or more phrases based upon a reference score in response to a determination that the machine learning score is less than the predetermined threshold score; and
store the one or more annotations at a relational database in response to predicting the one or more annotations.
16. The one or more non-transitory, computer-readable storage media of claim 15 , further comprising a plurality of instructions stored thereon that, in response to being executed, cause the server to collect the voice data from a voice assistant, wherein to receive the plurality of communications comprises to receive the plurality of communications in response to collecting the voice data.
17. The one or more non-transitory, computer-readable storage media of claim 15 , further comprising a plurality of instructions stored thereon that, in response to being executed, cause the server to collect the voice data from a voice-based survey, wherein to receive the plurality of communications comprises to receive the plurality of communications in response to collecting the voice data.
18. The one or more non-transitory, computer-readable storage media of claim 15 , further comprising a plurality of instructions stored thereon that, in response to being executed, cause the server to transcribe, by a transcription engine, the speech data to generate the voice data, wherein to receive the plurality of communications comprises to receive the plurality of communications in response to transcribing the speech data.