Clinical medicine- related sentiment analysis has advanced recently, as more and more researchers are performing studies with the help of this valuable technique, having noticed its ability to contribute in the field. Sentiment analysis is mostly applied in domains such as marketing and customer service but also in clinical medicine. Since customers express their thoughts and feelings more openly in our time, than ever before, sentiment analysis is becoming an essential tool to monitor and understand that sentiment. It’s often used by businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Sentiment analysis is the process of detecting positive or negative sentiment in text. Sentiment analysis, which is also referred to as ‘opinion mining’ or ‘emotion AI’, processes natural language, analyzes text and employs computational linguistics, and biometrics to identify and analyze emotions and subjective information ( 1). Out of these techniques, sentiment analysis is one of the most studied NLP topic. A plethora of studies apply different natural language processing (NLP) techniques.
Social media analysis is one of the most popular research areas nowadays.
We definitely have to deal with the manifestations on these platforms, and as machine learning becomes increasingly popular and significant just like the natural language processing (NLP), we have to deal with this, and analyze and study the emotions on this platforms. In many cases, the texts associated with the tweets are trended based on the real-time incidents and events. Twitter allow users to post texts (known as tweets) with up to 280- characters expressing thoughts, information, comments, and observations. These platforms have become very popular discussion forums for many users around the world. In recent years, social media platforms like twitter are of huge importance to people’s everyday lives.
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