Our client is a US-based start-up specializing in online reputation management for Health care providers like hospitals and private practices. The Client wanted a solution built using NLP and Machine Learning pipeline to analyze feedback from patients regarding their doctor’s visits. From the patient review, we extract entities, score them on the sentiment and rank the entities based on common positive and negative attributes. The result of the analysis is visualized in a dashboard.
- Trained a machine learning sentiment classifier to score entities as positive, negative and neutral from historic data.
- Built a custom NLP pipeline to identify and extract hidden entities in the review text and extract the sentences associated with the entities.
- The text related to the hidden entities is scored using the trained classifier.
- Trained a model to detect and extract the most common positive and negative attributes that has the highest correlation with review sentiment.
- The entities are ranked across these common positive and negatives attributes.
Our Award-Winning Team
A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better
Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0
Awarded as Winner among 1000 contestants at TechSHack Hackathon
Web scraping – A domain that is resonating across industries and businesses recently. Web scraping is one of the big businesses in the years to come.
In this modern world, the volume of unstructured data on the web is huge. This data explosion presents enormous opportunities for companies that can extract, manage, and analyze this data.
Data Scraping is basically a process of extracting data from a website using some scripts or automation tool/software. In this demo, we have to scrape the review and information about the doctors from various medical field-oriented websites using Scrapy and Selenium tools.