Natural Language Processing, Sentiment Analysis and Clinical Analytics

TL;DR

This paper explores how natural language processing and sentiment analysis can be applied to social media data to improve clinical analytics and mental health assessment, emphasizing NLP techniques and tools like NLTK.

Contribution

It reviews prevalent NLP theories, their application to social media sentiment analysis, and discusses how these methods can enhance healthcare insights and mental health monitoring.

Findings

Sentiment analysis can effectively gauge patient emotions from social media.

NLP techniques help reduce errors in data interpretation over time.

NLTK toolkit facilitates easier implementation of NLP methods.

Abstract

Recent advances in Big Data has prompted health care practitioners to utilize the data available on social media to discern sentiment and emotions expression. Health Informatics and Clinical Analytics depend heavily on information gathered from diverse sources. Traditionally, a healthcare practitioner will ask a patient to fill out a questionnaire that will form the basis of diagnosing the medical condition. However, medical practitioners have access to many sources of data including the patients writings on various media. Natural Language Processing (NLP) allows researchers to gather such data and analyze it to glean the underlying meaning of such writings. The field of sentiment analysis (applied to many other domains) depend heavily on techniques utilized by NLP. This work will look into various prevalent theories underlying the NLP field and how they can be leveraged to gather users sentiments on social media. Such sentiments can be culled over a period of time thus minimizing the errors introduced by data input and other stressors. Furthermore, we look at some applications of sentiment analysis and application of NLP to mental health. The reader will also learn about the NLTK toolkit that implements various NLP theories and how they can make the data scavenging process a lot easier.