1. SENTIMENT ANALYSISUSING NAÏVE BAYES CLASSIFIERCREATED BY:-DEV KUMAR , ANKUR TYAGI , SAURABH TYAGI(Indian institute of information technology Allahabad )10/2/2014 [Project Name]1 2. Introduction• Objectivesentimental analysis is the task to identify ane-text (text in the form of electronic data suchas comments, reviews or messages) to bepositive or negative.10/2/2014 [Project Name]2 3. MOTIVATION• Sentimental analysis is a hot topic of research.• Use of electronic media is increasing day by day.• Time is money or even more valuable than moneytherefore instead of spending times in reading andfiguring out the positivity or negativity of text wecan use automated techniques for sentimentalanalysis.• Sentiment analysis is used in opinion mining.– Example – Analyzing a product based on it’s reviewsand comments.10/2/2014 [Project Name]3 4. PREVIOUS WORK• There has been many techniques as an outcome ofongoing research work like• Naïve Bayes.• Maximum Entropy.• Support Vector Machine.• Semantic Orientation.10/2/2014 [Project Name]4 5. Problem DescriptionWhen we Implement a sentiment analyzer we cansuffer following problems.1. Searching problem.2. Tokenization and classification .3. Reliable content identification10/2/2014 [Project Name]5 6. Continue….Problem faced– Searching problem• We have to find a particular word in about 2500files.– All words are weighted same for example good andbest belongs to same category.– The sequence in which words come in test data isneglected. Other issues-– Efficiency provided from this implementation Is only40-50%10/2/2014 [Project Name]6 7. Approaches1.Naïve Bayes Classifier2.Max Entropy3.Support vector machine10/2/2014 [Project Name]7 8. Continue…• Naïve Bayes Classifier– Simple classification of words based on ‘Bayestheorem’.– It is a ‘Bag of words’ (text represented as collectionof it’s words, discarding grammar and order ofwords but keeping multiplicity) approach forsubjective analysis of a content.– Application -: Sentiment detection, Email spamdetection, Document categorization etc..– Superior in terms of CPU and Memory utilization asshown by Huang, J. (2003).10/2/2014 [Project Name]8 9. Continue…• Probabilistic Analysis of Naïve Bayesfor a document d and class c , By Bayes theoremP d c P c( / ) ( )Naïve Bayes Classifier will be - :10/2/2014 [Project Name]9( )( | )P dP c d c* argmaxc P(c | d) 10. Continue…10/2/2014 [Project Name]10Naïve Bayes ClassifierMultinomial Naïve BayesBinarized Multinomial Naïve Bayes 11. Continue…Multinomial Naïve Bayes ClassifierAccuracy – around 75%Algorithm - : Dictionary GenerationCount occurrence of all word in our whole data set andmake a dictionary of some most frequent words. Feature set Generation- All document is represented as a feature vector over thespace of dictionary words.- For each document, keep track of dictionary words alongwith their number of occurrence in that document.10/2/2014 [Project Name]11 12. Continue… Formula used for algorithms - :( | ) | P x k label y k label y j x label y1{ k and } 1k|label y = probability that a particular word in document oflabel(neg/pos) = y will be the kth word in the dictionary.= Number of words in ith document.= Total Number of documents.10/2/2014 [Project Name]12( 1{ } ) | |1( )1 1( ) ( )label y n Vmiiiminji iji k|label y i nm 13. Continue…i label yCalculate Probability of occurrence of each label .Here label isnegative and positive. These all formulas are used for training .10/2/2014 [Project Name]13mP label ymi 1( ) 1{ }( ) 14. Continue… TrainingIn this phase We have to generate training data(words withprobability of occurrence in positive/negative train data files ).Calculate for each label .Calculate for each dictionary words and store theresult (Here: label will be negative and positive).Now we have , word and corresponding probability for each ofthe defined label .10/2/2014 [Project Name]14P(label y)k|label y 15. Continue… TestingGoal – Finding the sentiment of given test data file.• Generate Feature set(x) for test data file.• For each document is test set findDecision1 log P(x | label pos) log P(label pos)• Similarly calculateDecision2 log P(x | label neg) log P(label neg)• Compare decision 1&2 to compute whether it hasNegative or Positive sentiment.Note – We are taking log of probabilities for Laplacian smoothing.10/2/2014 [Project Name]15 16. ˆP(c) =NcNcount w c( , ) 1count c V( ) | |ˆ ( | )P w cType Doc Words ClassTraining 1 Chinese Beijing Chinese cPriors:P(c)= 3/4P(j)= 1/4Conditional Probabilities:P( Chinese | c ) = (5+1) / (8+6) = 6/14 = 3/7P( Tokyo | c ) = (0+1) / (8+6) = 1/14P( Japan | c ) =(0+1) / (8+6) = 1/14P( Chinese | j ) =(1+1) / (3+6) = 2/9P( Tokyo | j ) =(1+1) / (3+6) = 2/9P( Japan | j ) =(1+1) / (3+6) = 2/92 Chinese Chinese Shanghai c3 Chinese Macao c4 Tokyo Japan Chinese jTest 5 Chinese Chinese ChineseTokyo JapanChoosing a class:P(c|d5) = 3/4 * (3/7)3 * 1/14 *1/14≈ 0.0003P(j|d5) = 1/4 * (2/9)3 * 2/9 * 2/9≈ 0.000110/2/2014 [Project Name] 16?An Example of multinomial naïve Bayes 17. Continue…Binarized Naïve BayesIdentical to Multinomial Naïve Bayes, Onlydifference is instead of measuring all occurrenceof a token in a document , we will measure it oncefor a document.Reason - : Because occurrence of the wordmatters more than word frequency and weightingit’s multiplicity doesn’t improve the accuracyAccuracy – 79-82%10/2/2014 [Project Name]17 18. 10/2/2014 [Project Name] 18
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