Description:
Sentiment Analysis in Healthcare A case study using survey responses Outline 1) Sentiment analysis & healthcare 2) Existing tools 3) Conclusions & Recommendations Focus on Healthcare 1) Difficult field biomedical text 2) Potential improvements Relevant Research: NLP procedure: FHF prediction (Roy et. al., 2013) TPA: Who is sick, Google Flu Trends (Maged et. al., 2010) BioTeKS: analyse biomedical text (Mack et. al., 2004) Sentiment Analysis Opinions Thoughts Feelings Used to extract information from raw data Sentiment Analysis Examples Surveys: analyse open-ended questions Business & Governments: assist in the decision-making process & monitor negative communication Consumer feedback: analyse reviews Health: analyse biomedical text Aims & Objectives Can existing Sentiment Analysis tools respond to the needs of any healthcare- related matter? Is it possible to accurate replicate human language using machines? The case study details 8 survey questions (open & close-ended) Analysed 137 responses based on the question: What is your feedback? Commercial tools: Semantria & TheySay Non-commercial tools: Google Predication API & WEKA Survey Overview 0 20 40 60 80 100 1 2 3 4 5 NumberofResponses Score Q.1: navigation Q.2: finding information Q.3: website's appeal Q.6: satisfaction Q.8: recommend website Semantria Collection Analysis Categories Classification Analysis Entity Recognition TheySay Document Sentiment Sentence Sentiment POS Comparison Detection Humour Detection Speculation Analysis Risk Analysis Intent Analysis Commercial Tools Results 39 51 47 Semantria Positive Neutral Negative 45 8 84 TheySay Positive Neutral Negative Introducing a Baseline 0 20 40 60 80 100 1 2 3 4 5 NumberofResponses Score Q.1 Q.2 Q.3 Q.6 Q.8 Neutral Classification Guidelines Equally positive & negative Factual statements Irrelevant statements Class Score Range Positive 1 2.7 Neutral 2.8 4.2 Negative 4.3 - 5 Introducing a Baseline Example Polarity Class CG 102 not available Hence: Negative Neutral Classification But Factual Statement Positive or negative? Final label: Neutral Q.1 Q.2 Q.3 Q.6 Q.8 Avg. 3 5 4 5 5 4.4 Introducing a Baseline 24 18 95 Manually Classified Responses Positive Neutral Negative Google Prediction API 1) Pre-process the data: punctuation & capital removal, account for negation 2) Separate into training and testing sets 3) Insert pre-labelled data 4) Train model 5) Test model 6) Cross validation: 4-fold 7) Compare with baseline Google Prediction API Results 5 122 10 Classification Results Neutral Negative Positive WEKA 1) Separate into training and testing sets 2) Choose graphical user interface: The Explorer 3) Insert pre-labelled data 4) Pre-process the data: punctuation, capital & stopwords removal and alphabetically tokenize WEKA 5) Consider resampling: whether a balanced dataset is preferred 6) Choose classifier: Nave
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