Patent AU-2021105938-A4 (AU)
Title: Automatic and dynamic contextual analysis of sentiment of social content and feedback reviews based on machine learning model
Inventor: NAGLE MALTI
IPC: G06Q30/00
Filed: 2021-08-19
Granted: 2021-12-09
Abstract: Automatic and dynamic contextual analysis of sentiment of social content 
and feedback reviews based on machine learning model 
ABSTRACT 
The present invention relates to the field of data processing and machine learning models.  
More specifically, the present invention relates to the field of automatic and dynamic 
analysis of sentiment of social content and feedback reviews on various aspects based on 
machine learning model (artificial neural network). In today's world, expanding and 
maintain growth of business, one need to have good customer relationship management.  
The complaints of customers need to be quickly resolved and business has to be evolved as 
per the customer requirements. Beside this, gathering and analyzing large amount of data 
(customer's feedback, social networking posts, blog posts, e-mails, chatroom discussions, or 
other electronic communications) about a product or business at large scale manually is also 
a big challenge. The proposed system is completely automatic, dynamic in nature and 
competent enough to gather data and train themselves based on machine learning models.  
The present system comprises sentiment processor module, social content and customer 
feedback retrieval module, social content and customer feedback crawl module, gathered 
data segmentation and treatment module and sentiment analysis module. The system 
dynamically and automatically collects data at predefined interval from various sources (i.e., 
customer's feedback, social networking posts, blog posts, e-mails, chatroom discussions, or 
other electronic communications) using social content and customer feedback retrieval 
module, social content and customer feedback crawl module. Then the proposed system 
segment and treat gathered data, and sentiment processor and analysis modules process 
and analyzes the data. The sentiment analysis module is based on machine learning 
technique which is basically a recurrent neural network. The machine learning model is 
trained using gathered sentiment data. A sentiment dictionary including multiple 
expressions mapped to multiple sentiment values representing different sentiments can be 
received. Each opinion/expression/feedback in the sentiment dictionary is mapped to a 
corresponding sentiment score value. The final sentiment for the communication (i.e., 
customer's feedback, social networking posts, blog posts, e-mails, chatroom discussions, or 
other electronic communications) can be determined using the sentiment dictionary.  
Training data usable for training the neural network can be automatically constructed based 
on the final sentiment value of a particular sentiment. The present system is also trained to 
provide customer's query response in real time basis using the sentiment data processed 
using the said system. Whenever, any customer asks query or complaint about any product 
or services, the system is trained enough to provide response or resolve query promptly so 
that a strong customer relationship can be maintained. The proposed system helps in 
maintain strong business growth and good customer relationship and provide efficient 
contextual sentiment analysis on various aspect automatically on large amount of data 
gathered from various sources.  
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Train central server using database and test cases according to machine learning 
model (101) 
crawling to the product/service related comments or social post, by social content 
and customer feedback crawl module using email or contact info (102) 
retrieving and storing the identified data to the databased on central server by 
the social content and customer feedback retrieval module (103) 
segmenting and determining the contextual analysis of the gathered data by 
gathered data segmentation and treatment module (104) 
determining the sentimental score of the gathered data in consultation with 
sentiment dictionary by sentiment analysis module (105) 
replying to the customer feedback based on the sentimental score of the gathered 
data (106) 
Figure 1: flow-chart of automatic and dynamic contextual analysis of sentiment 
method using machine learning model according to the present invention 
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