By using social media people can exchange sentiments and emotions, allowing to understand public opinion on specific issues. Sentiment Analysis (SA) is a novel text-mining (TM) and natural language processing (NLP) methodology to extract sentiment, opinions and emotions from written texts, usually provided through social media or questionnaires. Sharing medical and clinical experiences of patients through social media, is the target of the so-called Narrative Medicine (NM). Here we report some research experiences in applying SA techniques to analyze NM texts. A problem to be faced in NM is the automatic analysis of a potentially large set of documents. Application of SA is useful for having immediate analysis and extracting information from medical literature quickly. Here we present a software pipeline based on SA and TM which allows to effectively analyze NM texts. First experimental results allow to discover topics related to diseases.
A Software Pipeline Based on Sentiment Analysis to Analyze Narrative Medicine Texts
Scarpino I.;Zucco C.;Cannataro M.
2021-01-01
Abstract
By using social media people can exchange sentiments and emotions, allowing to understand public opinion on specific issues. Sentiment Analysis (SA) is a novel text-mining (TM) and natural language processing (NLP) methodology to extract sentiment, opinions and emotions from written texts, usually provided through social media or questionnaires. Sharing medical and clinical experiences of patients through social media, is the target of the so-called Narrative Medicine (NM). Here we report some research experiences in applying SA techniques to analyze NM texts. A problem to be faced in NM is the automatic analysis of a potentially large set of documents. Application of SA is useful for having immediate analysis and extracting information from medical literature quickly. Here we present a software pipeline based on SA and TM which allows to effectively analyze NM texts. First experimental results allow to discover topics related to diseases.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.