AI Integrated Patients Sentiment Analysis – Overview
Sentiment analysis is a method used in computational linguistics (NLP) to ascertain the emotional state of a document. It is sometimes referred to as information extraction. This is a popular strategy used by businesses to find and different groups of stakeholders on a certain product, service, or concept.
How AI Sentiment Analysis Works?
- Sentimental evaluation classify a text as Positive, Negative, or Neutral to recognize human opinions and emotions.
- Generally speaking, it combines the power of two AI subfields:
Automated Language Interpretation (NLP)
- While using natural language processing, machines can now comprehend human language (NLP). In order to comprehend how the text is organised, syntactic and semantic methods were being used (to identify meaning). Lemmatization, tokenization, and part-of-speech tagging are some of these approaches.After the text has been cleaned up using NLP methods, machine learning algorithms may classify it.
- Computers could now detect patterns in data and forecast events thanks to machine learning. So instead explicit instructions, machine learning algorithms get their cues from example that are close to them (training data).
- If you desire your model to be able to classify text according to sentiment, you must train it with examples of textual emotions. Each of these cases needs to be classified appropriately. To increase the model’s precision, a decent sample size is required for every tag.
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
- 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 the 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 have the highest correlation with review sentiment.
- The entities are ranked across these common positive and negative attributes.
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.