Sentiment Analysis of User-Generated Contents for Pharmaceutical Product Safety
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1 Sentiment Analysis of User-Generated Contents for Pharmaceutical Product Safety Haruna Isah, Daniel Neagu and Paul Trundle Artificial Intelligence Research Group University of Bradford, UK Haruna Isah is a Commonwealth Scholar Funded by the UK Government 8th July, 2015 MLCI-2015 DIBRIS
2 Outline Introduction Research Overview Product Safety Framework Data Collection, Preprocessing and Representation Sentiment Analysis Output Representation and Evaluations Case Studies Current Work Conclusion References Questions and Suggestions 2 MLCI-2015 DIBRIS
3 Introduction PhD Research Title: Knowledge Discovery in Drugs Anti-counterfeiting The problem: counterfeit products: deadly, financial losses current strategies (lab test and targeted operations): failed, but provide data for KD PhD Research Goal to harness counterfeit related data, observe, measure, develop models, algorithms, and systems for predictive anti-counterfeiting Proposed Approaches: sentiment analysis: what are pharmaceutical product users saying? network analysis & modelling: who are the counterfeiters? what medium do they use to advertise and sell fake products? any organisational/group structure among them? 3 MLCI-2015 DIBRIS
4 Introduction Sentiment Analysis: measure of opinions, moods, attitudes expressed in text, speech, facial emotions, etc. as user generated contents (blogs, wikis, forums, posts, chats, tweets, images, videos) applied in surveillance, disaster communication/management, market prediction, etc. consumers are now taking control of brands through user-generated content! businesses needs to listen to the voice of their customers! hence the need for sentiment analysis. sentiment analysis: text classification problem (scope of our work) given an input of document d, set of fixed classes C = {c 1,...c j } and a training set of m labelled documents (d 1, c 1 ),...(d m, c m ) the task is to model a classifier γ:d = c for predicting unlabelled documents challenge: labelling data is expensive 4 MLCI-2015 DIBRIS
5 Research Overview Research Goal: to extract and measure the textual sentiments of drug and cosmetic consumers Research Questions: What is the public sentiment about a given brand (s) of drug and cosmetic product? Can these sentiments be used as early clues for product counterfeit prediction? Contributions Product safety framework for harnessing and measuring sentiment orientation from text data. Training data annotation for machine learning sentiment analysis the output of the lexicon sentiment approach as training data for modelling a classifier. 5 MLCI-2015 DIBRIS
6 Product Safety Framework Stages: text collection and cleaning; pre-processing; sentiment analysis evaluation Sentiment Detection: Lexicon Machine Learning 6 MLCI-2015 DIBRIS
7 Data Collection, Preprocessing and Representation Data Collection Limited to textual data: updates and interactions on Twitter, posts and comments on Facebook, etc. Achieved by: invoking an API call for authentication and data extraction Twitter REST and Streaming API twitter and streamr libraries Facebook Graph API Rfacebook library The packages provide functions for access & extraction of information about users, posts, comments, keywords, etc via Twitter and Facebook APIs. 7 MLCI-2015 DIBRIS
8 Data Collection, Preprocessing and Representation Preprocessing and Representation Text data into feature vectors or bag of words. In R, the tm library is used for: converting the text to lowercase, removing numbers and punctuations, removing stop words, stemming, identifying synonyms, vectorisation, etc. 8 MLCI-2015 DIBRIS
9 Sentiment Analysis Lexicon-based approach: require labelled word list or polarity lexicon and sentiment scoring function/scheme custom = generic + domain specific (prepared by domain expert or generated from frequent word analysis) scoring: simple difference between -ive and +ive vectors, fuzzy scoring, etc. 9 MLCI-2015 DIBRIS
10 Sentiment Analysis Machine learning approach: require a training dataset, coded with sentiment classes. the classifier is trained with the labeled data, the classifier is then used to predict the sentiment of new but similar texts. 10 MLCI-2015 DIBRIS
11 Output Representation and Evaluations Sentiment polarity result: can be represented as a histogram or box plots of polarity measures can be evaluated with reference to a ground truth or by human judgment. The sentiment classifier is utilised to predict the sentiment orientation of unseen data. Classifier accuracy and performance evaluation: contingency tables or truth table are used to represent the output of a classifier baseline results such as [Pang et al], [Pang & Lee], and [Apoorv et al] are used for performance comparison. 11 MLCI-2015 DIBRIS
12 Case Studies Case Study 1: Brand and Product Lexicon Sentiment Analysis User comments and opinions from Avon, Dove and OralB Facebook pages 3 datasets randomly coded as Brand X, Brand Y and Brand Z. 3 datasets in Brand Y with the mention of 3 products coded as Product 1, 2 & 3 Tasks: retrieval of public contents/adverts of the targeted pages using Rfacebook library extraction of user comments from popular posts user feedbacks: Brand X offer a prize in return while Brand Y and Z do not. preprocessing using text mining (tm) library frequent term analysis = preassembled lexicon preassembled + generic[lex][affective] + social media slangs = custom lexicon comparison lexicon sentiment analysis 12 MLCI-2015 DIBRIS
13 Case Studies Brand Comparison Interesting result: all 3 brands: +ively skewed neg:neu:pos for X, Y and Z = 1:42: 175, 1:3:3 and 1:5: 5 as expected: Brand X feedback is very high in +ive sentiment due to the prize offer. 13 MLCI-2015 DIBRIS
14 Case Studies Product Comparison Product 3 more +ively skewed than 1 and 2 neg:neu:pos for 1, 2 and 3 =1:2: 2, 1:1:2 and 1:2: 5 A significant increase in ive sentiment may demand an urgent action 14 MLCI-2015 DIBRIS
15 Case Studies Case Study 2: Machine Learning Sentiment Classification Classifying the sentiment orientation of the entire Facebook datasets with a naive Bayes classifier modelled with polarity and emotion lexicon. sentiment library archived in R polarity: positive, neutral or negative emotion: anger, disgust, fear, joy, sadness, and surprise. Comparison of the classification results over the entire corpus for lexicon and machine learning methods provided some interesting results. -tive scores for both methods agree closely while there is a sharp variation in both the neutral and +itive scores. 15 MLCI-2015 DIBRIS
16 Case Studies Case Study 3: Modelling Machine Learning Sentiment Classifier predicting the sentiment of a given text Tasks: data collection, cleaning, preprocessing, and bag of words representation 11,431 tweets based on pharma and medical related keyword combinations lexicon sentiment classification output: 11,431 tweets classified with corresponding sentiment scores scores > 0, +tives & scores < 0, -tives output = training data for modelling a machine learning classifier machine learning sentiment classification input: output of the above lexicon sentiment classification 16 MLCI-2015 DIBRIS
17 Case Studies training: using e1071 library the Bayes classification algorithm is quite simple it uses the presence or absence of words to estimate the probability of the target class of tweets. it relies on a very simple representation of the document called the bag of words representation. other text classification algorithms: SVM, ANN, etc. evaluation: holdout method (because of speed only), 75% training & 25 % test data; accuracy 83% gmodels library used to generate confusion matrix for comparing the predicted values with the actual values. Laplace estimator values were used to improve the classification performances. 17 MLCI-2015 DIBRIS
18 Current Work My current work is centred around the design and development of models and algorithms for crime intelligence in the cyberspace. I employ a variety of graph and link mining methods to characterise and combat cybercrime on the Web. The Problem: Vulnerability of Web search and ranking algorithms to manipulations by commercial interests. Web and Social Spamming Community Structure or Organisational Strategy of Cybercriminals Proposed Models: Bipartite model for uncovering hidden tie in crime data: undergoing peer review 18 MLCI-2015 DIBRIS
19 Conclusion The global scourge of counterfeit products poses a great threat to public safety. When text mining and sentiment analysis techniques are combined in a project on social media data, the result is often a powerful descriptive or predictive tool. Developing models that can describe, measure, predict why certain brands or products may be counterfeited in space and time are very important scientific and practical problems. Future work: large data, compare result of different classification algorithms and model selection methods, use of non textual data MLCI-2015 DIBRIS
20 Conclusion Collaborative Opportunities Large Scale Data Analysis with R and Hadoop Developing Eigenvalue and Large Matrix Solvers Developing Measures and Strategies for Mitigating the Manipulation of Popular Ranking Algorithms such as PageRank and HITS. Models and Algorithms for Measuring and Combating Web and Social Spamming. Etc. 20 MLCI-2015 DIBRIS
21 References [Haruna et al] Social media analysis for product safety using text mining and sentiment analysis [Haruna] Social Media Analysis for Combating Counterfeit Products [Pang et al] Thumbs up?: sentiment classification using machine learning techniques [Pang & Lee] A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts [Apoorv et al] Sentiment Analysis of Twitter Data [Lex] Sentiment Symposium Tutorial: Lexicons [Affective] Affective ratings for nearly 14 thousand English words 21 MLCI-2015 DIBRIS
22 Suggestions and Questions Thanks? Suggestions Collaboration 22 MLCI-2015 DIBRIS
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