This ppt contains a small description of naive bayes classifier algorithm. It is a machine learning approach for detection of sentiment and text classification.

1. SENTIMENT ANALYSIS
USING NAÏVE BAYES CLASSIFIER
CREATED BY:-
DEV KUMAR , ANKUR TYAGI , SAURABH TYAGI
(Indian institute of information technology Allahabad )
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2. Introduction
• Objective
sentimental analysis is the task to identify an
e-text (text in the form of electronic data such
as comments, reviews or messages) to be
positive or negative.
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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 money
therefore instead of spending times in reading and
figuring out the positivity or negativity of text we
can use automated techniques for sentimental
analysis.
• Sentiment analysis is used in opinion mining.
– Example – Analyzing a product based on it’s reviews
and comments.
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4. PREVIOUS WORK
• There has been many techniques as an outcome of
ongoing research work like
• Naïve Bayes.
• Maximum Entropy.
• Support Vector Machine.
• Semantic Orientation.
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5. Problem Description
When we Implement a sentiment analyzer we can
suffer following problems.
1. Searching problem.
2. Tokenization and classification .
3. Reliable content identification
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6. Continue….
Problem faced
– Searching problem
• We have to find a particular word in about 2500
files.
– All words are weighted same for example good and
best belongs to same category.
– The sequence in which words come in test data is
neglected. Other issues-
– Efficiency provided from this implementation Is only
40-50%
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7. Approaches
1.Naïve Bayes Classifier
2.Max Entropy
3.Support vector machine
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8. Continue…
• Naïve Bayes Classifier
– Simple classification of words based on ‘Bayes
theorem’.
– It is a ‘Bag of words’ (text represented as collection
of it’s words, discarding grammar and order of
words but keeping multiplicity) approach for
subjective analysis of a content.
– Application -: Sentiment detection, Email spam
detection, Document categorization etc..
– Superior in terms of CPU and Memory utilization as
shown by Huang, J. (2003).
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9. Continue…
• Probabilistic Analysis of Naïve Bayes
for a document d and class c , By Bayes theorem
P d c P c
( / ) ( )
Naïve Bayes Classifier will be - :
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( )
( | )
P d
P c d 
c*  argmaxc P(c | d)

10. Continue…
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Naïve Bayes Classifier
Multinomial Naïve Bayes
Binarized Multinomial Naïve Bayes

11. Continue…
Multinomial Naïve Bayes Classifier
Accuracy – around 75%
Algorithm - :
 Dictionary Generation
Count occurrence of all word in our whole data set and
make a dictionary of some most frequent words.
 Feature set Generation
- All document is represented as a feature vector over the
space of dictionary words.
- For each document, keep track of dictionary words along
with their number of occurrence in that document.
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12. Continue…
 Formula used for algorithms - :
( | ) | P x k label y k label y j     
x label y
1{  k and  } 
1

k|label y 
= probability that a particular word in document of
label(neg/pos) = y will be the kth word in the dictionary.
= Number of words in ith document.
= Total Number of documents.
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( 1{ } ) | |
1
( )
1 1
( ) ( )
label y n V
m
i
i
i
m
i
n
j
i i
j
i
 



 
k|label y 
i n
m

13. Continue…
i  
label y
Calculate Probability of occurrence of each label .Here label is
negative and positive.
 These all formulas are used for training .
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m
P label y
m
i
  1
( ) 1{ }
( )

14. Continue…
 Training
In this phase We have to generate training data(words with
probability of occurrence in positive/negative train data files ).
Calculate for each label .
Calculate for each dictionary words and store the
result (Here: label will be negative and positive).
Now we have , word and corresponding probability for each of
the defined label .
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P(label  y)
k|label y 

15. Continue…
 Testing
Goal – Finding the sentiment of given test data file.
• Generate Feature set(x) for test data file.
• For each document is test set find
Decision1  log P(x | label  pos)  log P(label  pos)
• Similarly calculate
Decision2  log P(x | label  neg)  log P(label  neg)
• Compare decision 1&2 to compute whether it has
Negative or Positive sentiment.
Note – We are taking log of probabilities for Laplacian smoothing.
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16. ˆP(c) =
Nc
N
count w c
( , ) 
1
count c V
( ) | |
ˆ ( | )
P w c


Type Doc Words Class
Training 1 Chinese Beijing Chinese c
Priors:
P(c)= 3/4
P(j)= 1/4
Conditional Probabilities:
P( Chinese | c ) = (5+1) / (8+6) = 6/14 = 3/7
P( Tokyo | c ) = (0+1) / (8+6) = 1/14
P( Japan | c ) =(0+1) / (8+6) = 1/14
P( Chinese | j ) =(1+1) / (3+6) = 2/9
P( Tokyo | j ) =(1+1) / (3+6) = 2/9
P( Japan | j ) =(1+1) / (3+6) = 2/9
2 Chinese Chinese Shanghai c
3 Chinese Macao c
4 Tokyo Japan Chinese j
Test 5 Chinese Chinese Chinese
Tokyo Japan
Choosing a class:
P(c|d5) = 3/4 * (3/7)3 * 1/14 *
1/14
≈ 0.0003
P(j|d5) = 1/4 * (2/9)3 * 2/9 * 2/9
≈ 0.0001
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?
An Example of multinomial naïve Bayes

17. Continue…
Binarized Naïve Bayes
Identical to Multinomial Naïve Bayes, Only
difference is instead of measuring all occurrence
of a token in a document , we will measure it once
for a document.
Reason - : Because occurrence of the word
matters more than word frequency and weighting
it’s multiplicity doesn’t improve the accuracy
Accuracy – 79-82%
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