AI Integrated Patients Sentiment Analysis

AI Integrated Patients Sentiment Analysis — Overview

Sentiment analysis, often known as information extraction, is a technique used in natural language processing (NLP) to determine the emotional tone of a document. This is a common method used by organisations to identify and group ideas regarding a certain product, service, or idea.

How AI Sentiment Analysis Works?

• Sentiment analysis classify a given text as Positive, Negative, or Neutral to detect opinions and emotions automatically.
• In general, it combines the strength of two branches of AI:
• Natural Language Processing (NLP)
• Machine learning

• Natural language processing has made it possible for machines to understand human language (NLP). It uses syntactic and semantic techniques to understand the organisation of the text (to identify meaning). Some of these techniques include part-of-speech tagging, tokenization, and lemmatization.
• After the text has been cleaned up using NLP methods, machine learning algorithms may classify it.
• Machine learning and enables computers to identify patterns in data and anticipate outcomes. Machine learning algorithms take their cues from instances that are comparable to them rather than from explicit instructions (training data).
• You must train your model with instances of textual emotions if you want it to be able to categorise text according to sentiment. These examples must each be tagged with the appropriate category. A representative sample size for every tag is required to increase the model’s accuracy.

Benefits of Sentiment Analysis

• Enhance your customer service
• Finding New Marketing Techniques
• Consolidate media perceptions
• Revenue from Sales Growing
• Live information
• identifying the main emotional causes

How we helped Healthcare Industry with our sentiment analysis Solution

Solution overview

• We have worked with a Healthcare clinic in helping them understand their patient experience through the feedback aggregated from posts by patients on social media and online directories.
• Information like attributes on the physicians, nurses, support staff, hospital facility, etc., are extracted from these reviews and actionable insights are provided to the stakeholders to improve the patient experience.
• The ML and AI Based Patient Sentiment Analysis in Healthcare model is trained to detect and extract the most common positive and negative attributes that have the highest correlation with review sentiment.
• 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 negative attributes.

Technology

Market size: Sentiment Analysis

In the changed post COVID-19 business landscape, the global market for Sentiment Analytics estimated at US$2.7 Billion in the year 2020, is projected to reach a revised size of US$6.7 Billion by 2027, growing at a CAGR of 14.1% over the analysis period 2020–2027