Natural Language Processing, Sentiment Analysis and Clinical Analytics
It addresses the problem of improving mental health diagnosis and monitoring for healthcare practitioners by leveraging social media data, but it appears incremental as it reviews existing theories and tools without presenting new results.
This paper explores how Natural Language Processing (NLP) and sentiment analysis can be applied to analyze social media data for health informatics and clinical analytics, aiming to gather patient sentiments over time to reduce errors from traditional methods like questionnaires.
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.