Sentiment analysis in healthcare

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This presentation compares four tools for analysing the sentiment in the content of free-text survey responses concerning a healthcare information website. It was completed by Despo Georgiou as part of her internship at UXLabs (http://uxlabs.co.uk)

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<ul><li> Sentiment Analysis in Healthcare A case study using survey responses </li> <li> Outline 1) Sentiment analysis &amp; healthcare 2) Existing tools 3) Conclusions &amp; Recommendations </li> <li> 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) </li> <li> Sentiment Analysis Opinions Thoughts Feelings Used to extract information from raw data </li> <li> Sentiment Analysis Examples Surveys: analyse open-ended questions Business &amp; Governments: assist in the decision-making process &amp; monitor negative communication Consumer feedback: analyse reviews Health: analyse biomedical text </li> <li> Aims &amp; 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? </li> <li> The case study details 8 survey questions (open &amp; close-ended) Analysed 137 responses based on the question: What is your feedback? Commercial tools: Semantria &amp; TheySay Non-commercial tools: Google Predication API &amp; WEKA </li> <li> 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 </li> <li> Semantria Collection Analysis Categories Classification Analysis Entity Recognition </li> <li> TheySay Document Sentiment Sentence Sentiment POS Comparison Detection Humour Detection Speculation Analysis Risk Analysis Intent Analysis </li> <li> Commercial Tools Results 39 51 47 Semantria Positive Neutral Negative 45 8 84 TheySay Positive Neutral Negative </li> <li> 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 &amp; negative Factual statements Irrelevant statements Class Score Range Positive 1 2.7 Neutral 2.8 4.2 Negative 4.3 - 5 </li> <li> 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 </li> <li> Introducing a Baseline 24 18 95 Manually Classified Responses Positive Neutral Negative </li> <li> Google Prediction API 1) Pre-process the data: punctuation &amp; 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 </li> <li> Google Prediction API Results 5 122 10 Classification Results Neutral Negative Positive </li> <li> 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 &amp; stopwords removal and alphabetically tokenize </li> <li> WEKA 5) Consider resampling: whether a balanced dataset is preferred 6) Choose classifier: Nave Bayes 7) Classify using cross validation: 4-fold </li> <li> WEKA Results Resampling: 10% increase in precision 6% increase in accuracy Overall, 82% correctly classified </li> <li> The tools Semantria: range between -2 and 2 TheySay: three percentages for negative, positive &amp; neutral Google Prediction API: three values for negative, positive &amp; neutral WEKA: percentage of correctly classified </li> <li> Evaluation Tool Accuracy Commercial Tools Semantria 51.09% TheySay 68.61% Non-Commercial Tools Google Prediction API 72.25% WEKA 82.35% </li> <li> Evaluation Tool Kappa statistic F-measure Semantria 0.2692 0.550 TheySay 0.3886 0.678 Google Prediction API 0.2199 0.628 WEKA 0.5735 0.809 </li> <li> Evaluation </li> <li> Evaluation 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Negative Neutral Positive PrecisionValue Class Comparison of Precision Semantria TheySay Google API WEKA </li> <li> Evaluation 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Negative Neutral Positive RecallValue Class Comparison of Recall Semantria TheySay Google API WEKA </li> <li> Evaluation: Single-sentence responses Tool Accuracy based on correct classification All responses Single- sentence Responses Commercial Tools Semantria 51.09% 53.49% TheySay 68.61% 72.09% Non-Commercial Tools Google Prediction API 72.25% 54% WEKA 82.35% 70% </li> <li> Conclusions Semantria: business use TheySay: prepare for competition &amp; academic research Google Prediction API: classification WEKA: extraction &amp; classification in healthcare </li> <li> Conclusions Commercial tools: easy to use and provide results quickly Non-commercial tools: time-consuming but more reliable </li> <li> Conclusions Is it possible to accurate replicate human language using machines? Approx. 70% accuracy for all tools (except Semantria) WEKA: most powerful tool </li> <li> Conclusions Can existing SA tools respond to the needs of any healthcare-related matter? Commercial tools can not respond Non-commercial can be trained </li> <li> Limitations Only four tools Small dataset Potential errors in manual classification Detailed analysis of single-sentence responses was omitted </li> <li> Recommendations Examine reliability of other commercial tools Investigate other non-commercial tools, especially NLTK and GATE Examine other classifiers (SVM &amp; MaxEnt) Investigate all WEKAs GUI </li> <li> Recommendations Verify labels using more people Label sentence as well as the whole response Negativity associated with long reviews </li> <li> Questions </li> </ul>