Opinion mining and Setiments Analysis

1. Opinion mining and sentiment analysis
By
P Y SHASHI KISHORE
M-tech(SE)

2. Abstract
 Psychological phenomena for seeking opinions of others in
terms of global communication of people has gained a great
importance with the growth of internet technology
 A lot of excitement among public different stakeholders
towards the issues on political news, marketing strategies,
buyers preferences, companies profit/loss
 Opinion mining is a concept of implementing NLP techniques
on user input given to system via internet sources.
Sentiment analysis is used to extract emotions, to extract subject
on issue, and to find out the impact on user quoted mined
opinions.

3. LITERATURE SURVEY
Introduction
Background work
Availability of current systems

4. Introduction
 Internet has became a resource to perform
our activities like online buisness ,
information acquistion, community
operations etc.
 good number of companies large and small
are having opinion mining and sentiment
analysis as there part of mission
 profound applications of OMSA has urged
the research to gain its important rapidly

5. Background work
Document-level sentiment analysis
 Sentence -level sentiment analysis
Aspect- based sentiment analysis

6. Availability of current systems
With the rapid development of research in tis
area there are several sentiment tools ae
available in the market like LIWC, senti-word
net, senti strength tools etc.
Analyzing of sentiment is been complex in
chinese , arabic ,european ,and other
subcontinent languages

7. DESIGN PART
architecture of opinion mining and sentiment analysis
system

8. Users of the system with respect to their roles.

9. DESIGN MODULES
• Opinion Retrieval module
• Sentiment classification
• Summary generation

10. DESIGN MODULES
• User interface for retrieving opinions in ORM

11. DESIGN MODULES
• User interface for retrieving opinions in ORM
/Web scraping agent
/Twitter Web server
1 : "http://search.twitter.com/search.json?q="()
2 : OK()
3 : Fill search form with your keyword for tweets()
4 : ok if tweets found else no tweets()
5 : Extract reviews()
6 : Store/process reviews()

12. DESIGN MODULES
• User interface for retrieving opinions in WSM

13. DESIGN MODULES
• User interface for analyzing neutral
sentiments

14. OPINION RETRIEVAL MODULE
• Functional approach of this module is to mine opinions by text analytics.
such that these Opinions can be fetched as input by the classification
algorithm to determine the polarity of opinion text at sentence level.
Structuring of sentences:
Replace line ending with
spaces
Str_replace(r ,n,””,$string)
Breaks the sentences
document into individual
words or tokens
Explode(“ “,$sentence)
Removes any slashes if found
in the string
Stripcslashes($word)
‘’
Strips whitespaces or other
characters from beginning
and ending of the string
_cleanstring(“ “ t n o O
xoB ,””,$sentence)
Converts string to lower case
characters
Strtolower($string)

15. NEGATION RULES
• if a prefix/negation word appears before
negative word the orientation of negative word is
changed to positive word.
• if a prefix/negation word appears before positive
word the orientation of positive word is changed
to negative word.
• if a prefix/negation word appears before neutral
word the orientation of neutral word is changed
to negative word.

16. SENTIMENT CLASSIFICATION MODULE
Functional approach of this module is to fetch opinionated sentences from
opinion retrieval module and provide as input to the semi-supervised NB
classifier to derive the semantic orientation of the opinionated sentences.
Semi supervised NB classifier works on traditional Bayesian rule
• Illustration of Bayesian theorem
• P(h/d)=P(d/h)/p(d)
• Where, P(h) is the prior probability of hypothesis h
• P(d) prior probability of training data
• P(h/d) probability of h given d
• P(d/h) probability of d given h

17. Naive Bayes classifier
 Bayesian classifiers are based around the Bayes rule, a way of looking at
conditional probabilities that allows you to flip the condition around in a
convenient way. A conditional probably is a probably that event X will occur, given
the evidence Y. That is normally written P(X | Y). The Bayes rule allows us to
determine this probability when all we have is the probability of the opposite
result, and of the two components individually: P(X | Y) = P(X)P(Y | X) / P(Y)
 So, our initial formula looks like this.
 P(sentiment | sentence) = P(sentiment)P(sentence | sentiment) / P(sentence)
 we estimate P(token | sentiment) as
 count(this token in class) + 1 / count(all tokens in class) + count( all tokens )

18. Semi-supervised naive bayes
SSNB enhances the performance of simple NB classifier by considering the
linguistic word count LIWC of unseen words in the opinion sentence and
there after it performs multinomial distribution with the newly unseen
linguistic word counts of opinion sentence, while retraining the classifier
up on the classification of traditional naïve bayes to determine the polarity
of a opinion sentence.
• The SSNB classifier is illustrated in following steps:
• Lexical based datasets
• LIWC in bootstrapping process
• Supervised learning to semi-supervised learning

19. Semi-supervised naive bayes
Supervised learning to semi-supervised learning:
 count of opinion words in supervised naive bayes
Number of
positive
token(opinion
word) counts
Number of
negative
token(opinio
n word)
counts
Number of
neutral
token(opinion
word) counts
Number of
unlabelled
token(opinion
word) counts
X Y Z N

20. Supervised learning to semi-supervised learning:
 Now, we label unlabelled data in SSNB and accumulate the
linguistic count of opinion words as
Sentence
orientation
determined
with
supervised
naive bayes
Number of
positive
token(opinio
n word)
counts
Number of
negative
token(opinio
n word)
counts
Number of
neutral
token(opinio
n word)
counts
If found
positive
sentence
orientation
X + N Y + N + X(0.1) Z + N(0.1)
If found
negative
sentence
orientation
X + N + Y(0.1) Y + N Z + N(0.1)
If found
neutral
sentence
orientation
X + N(0.1) Y + N(0.1) Z + N

21. • After accumulating the count for possible unseen data in
given sentences we perform multinomial distributions in the
classifier to improve classifier performance.
• The multinomial distribution for SSNB is illustrated as :
• P(SENTENCE/SENTIMENT) = N! * (P(W1/SENTIMENT)F(W
1
)/
F(W1)! * P(W2/SENTIMENT)F(W
2
)/ F(W2)! * ………………..*
P(Wn/SENTIMENT)F(W
n
)/ F(Wn)! )
• Where N= length of sentence comprising opinion words
• F(Wn) = frequency of words
• W1 ,…………, Wn are opinion words identified in the sentence
respectively.

22. Implementation work
List of Modules in the project
Registration&Login
Twitter sentiment Analysis
Rating of customer Feedback
Analyzing of neutral sentiments
Changing sentiment status using POS

23. Registration&Login

24. Twitter sentiment Analysis

25. Code for tweets
<?php
$search = $_REQUEST['query'];
//echo $twitteruser;
$type=$_REQUEST['type'];
$consumerkey = "OlVstO8GmliOu6J4mhg";
$consumersecret = "ZDe6XdlIHxM8bo87KgPR5ajuiaoLZ86KacEcRBxw2U";
$accesstoken = "1288064322-LDz5QE08EjrixKFIHvJb6pRX4oBIZePHFvIsdgp";
$accesstokensecret ="OAYHKVdRwFq7V33vCuTR8G3rbzQiWyk3JmH4SsMXBk";
function getConnectionWithAccessToken($cons_key, $cons_secret, $oauth_token,
$oauth_token_secret) {
$connection = new TwitterOAuth($cons_key, $cons_secret, $oauth_token,
$oauth_token_secret);
return $connection;
}
$connection = getConnectionWithAccessToken($consumerkey, $consumersecret,
$accesstoken, $accesstokensecret);
$tweets = $connection->get("https://api.twitter.c

26. Code for tweets sentiment
if (isset($this->dictionary[$token][$class])) {
//Set count equal to it
$count = $this->dictionary[$token][$class];
}
else {
$count = 0;
}
//Score[class] is calcumeted by
$scores[class] x $count +1 divided by the $classTokCounts[class] + $tokCount
$scores[$class] *= ($count + 1);
}
}

27. Code for tweets sentiment
$scores[$class] = $this->prior[$class] * $scores[$class];
}
//Makes the scores relative percents
foreach ($this->classes as $class) {
$total_score += $scores[$class];
}
foreach ($this->classes as $class) {
$scores[$class] = $scores[$class] / $total_score;
}
//Sort array in reverse order
arsort($scores);
return $scores;
}

28. Tweets with sentiment

29. Summary of customer feedback

30. Analyzing of neutral sentiments

31. Conclusion:
Opinions are primary sources by which we can analyze the sentiments of people.
The evaluation of the system has found to be very effective with domain specific
using automatic algorithms and with the help of manual perceptions providing high
degree of accuracy.
However, a opinion word can express different meaning when used in different
domains and might raise disambiguous complex problems, which lead to
misclassification by the classifier. and also translation of any native languages like
Chinese ,Arabic, and other European languages into machine languages is a complex
process for linguistic approaches.