NLP Pipeline for Voice Data Sentiment Analysis
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Solution Overview
Problem
Current systems fail to efficiently process and analyze structured and unstructured experience data from diverse sources like medical records, surveys, and social media, leading to inaccurate and ineffective feedback analysis, particularly in healthcare and employee engagement, due to the complexity of language and varied data formats.
Innovation Solution
A system utilizing a hybrid Natural Language Processing (NLP) pipeline combined with machine learning and crowd sourcing to identify sentiments, themes, and named entities within the data, transforming unstructured data into structured and ordered information for visualization on user dashboards, providing actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used to analyze experience data from diverse sources, then the system is simpler to implement, but the accuracy and effectiveness of feedback analysis deteriorates due to language complexity and varied data formats
Solution Approach 1:
The patent segments the complex data processing task into distinct functional modules: data collection module, data cleaning module, natural language processing module, sentiment analysis module, and visualization module. Each module handles a specific aspect of the processing pipeline, making the overall system more manageable and effective despite the complexity of handling diverse data sources with varied formats and language complexities.
2Loss of information
If multiple data sources are processed and combined, then the comprehensiveness of analysis improves, but the time required for data processing and analysis increases
Solution Approach 1:
The patent implements preliminary data cleaning and preprocessing operations before the main analysis tasks. The data cleaning module standardizes formats and removes irrelevant information in advance, while the NLP module pre-processes text data into structured formats. This preliminary action reduces the computational burden during subsequent sentiment analysis and visualization stages, thereby reducing overall processing time while maintaining comprehensive analysis of multiple data sources.
3Measurement precision
If manual analysis of unstructured data is performed, then the interpretation accuracy improves, but the productivity and efficiency of processing large volumes of data deteriorates
Solution Approach 1:
The patent introduces natural language processing algorithms and sentiment analysis algorithms as intermediary tools between raw unstructured data and human interpretation. These intermediaries automatically process large volumes of unstructured text data from multiple sources, extracting meaningful insights and sentiment patterns with high accuracy. The system then presents processed results in visualized formats that maintain interpretability, thereby achieving both high productivity through automation and high interpretation accuracy through sophisticated NLP techniques.
Data Source
AI summary
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.