This document discusses opinion mining and sentiment analysis. It begins with introductions to sentiment, opinion mining, and the motivation for opinion mining including analyzing large amounts of opinionated online text. It then discusses challenges in opinion mining including distinguishing subjects and targets. It describes classifying sentiment at the word, sentence and document levels. Applications mentioned include information extraction, product reviews, and tracking sentiments. The document provides an overview of key concepts in opinion mining and sentiment analysis.

1. Mining and Sentiment Analysis
Presented By:
DIVYA JAIN
Under the Guidance of:
DR.S.F.SAYYAD
AISSMS COE , PUNE

2. Points to be Discussed…….
 Introduction
 Opinion Mining
 Motivation for Opinion Mining
 Challenges in Opinion Mining
 Sentiment Classification
 Document Sentiment Classification
 Sentence Subjectivity
 Applications

3. INTRODUCTION :
• A Sentiment is a thought, view, or attitude, especially one based
mainly on emotion instead of reason.
• There are usually two types of information available on the web
which can be said as : Facts and Opinion.
• Sentiment can be also called by other names as
1. Opinion Mining
2. Opinion extraction
3. Subjectivity Analysis
• The opinion mining is computational study of opinions, sentiments
and emotions which can be expressed in text.
• The most important reason behind using Opinion mining is huge
amount of opinionated text available on the web.

4. OPINION MINING :
 Opinion is very important for us because whenever we have to make
any important decision at that time we seeks opinion of other people
for business purposes and many more.
 Expression of opinion is not limited now a days ,it can be in the forms
of interviews, reports, question-answer format, forms , discussion ,
blogs etc.
 It uses the natural language processing (NLP) and computational
techniques to automate the extraction or classification of sentiment
from typically unstructured text.
 For opinion mining we can use the various approaches as:
1. Machine Learning
-> Naïve bayes , Support Vector Machine , Entropy Classificaton
2. By unsupervised Learning
-> Using lexicons

5. Motivation of opinion mining :
The motivation behind using the opinion mining is
Buisness and Organization
-product and service benchmarking , Market selling
Opinion Retrival/ Information Search
- a general search
Ad placement
-placing ad in user generated content
People
-opinion regarding new products.

6. Opinion Mining :General Model
 Opinion holder (source) :person who holds the sentiment.
E.g. I love playing hockey.
E.g I agree to what pope said “hate the sin not the sinners”
 Object (Target) :product, person, organization or a topic on which
sentiment is expressed.
E.g. I like nano. But I don’t like the steering of nano.
 Opinion/sentiment a view or appraisal on an object
E.g. It’s a pity(negative) that she didn’t marry.

7. Challenges in the Opinion Mining :
Contrasts with standard text-based categorization
Domain dependent
Identifying source and target:
Differentiating feature and attributes
“I hate the ipod but I like the scroll technology”
Role of semantics
“How could anyone sit through this movie?”
 Issue of Ideology

8. Sentiment Classification
 Basically sentiments are classified into 4 levels
 Sentiment Classification at Word Level
 Sentence Level Sentiment Analysis
 Sentiment Classification at Document Level
 Comparative Sentence Analysis

9. Word Level Sentiment Classification :
Used for grammatically coherent text like short news paper
headlines. It can be directly computed using the lexicon resources , for
eg : SentiWordNet.
Sentence level Sentiment Analysis:
For Sentiment analysis at Sentence level contextual information
is necessary. It can not assign prior polarity to all words.
Document Level Sentiment Classification :
It classifies the document as positive or negative . And
classification is based on the overall sentiments which author holds.
Comparative Sentence Analysis:
It is known as preferential emotion detection. For eg “ I like X
to Y “.Most common feature is use of Comparative word like as to ,
better etc.

10. Document level Sentiment Classification:
 In the stage , documents are classified in positive or negative (may be
neutral) on the basis of opinion expressed by opinion holders.
 It assumes
 Each document focuses on a single object and contains opinions
from a single opinion holder.
 It considers opinion on the object, oj (or oj = fjk)
Where oj = target object
fjk = feature of object oj
 For eg: Travelling Review  Recommended / Not Recommended

11. Document Level Classification …..
Extract phrases - 2 words long(with context) e.g. good
place
Calculate the semantic orientation of extracted phrases
using PMI with “excellent” & “poor”
Classification based on Average semantic orientation of
the phrases
Classify based on average threshold
SO(Phrase) = 





excellent)poor)hits(nearehits(phras
hits(poor)excellent)nearehits(phras
log

12. Sentence Subjectivity :
 Sentence-level sentiment analysis has two tasks:
 Subjectivity classification: Subjective or objective.
 Objective: e.g., I bought an iPhone a few days ago.
 Subjective: e.g., It is such a nice phone.
 Sentiment classification: For subjective sentences or clauses,
classify positive or negative.
 Positive: It is such a nice phone.

13.  The linguistic expression of somebody’s opinions, sentiments,
emotions, evaluations, beliefs, speculation.
 Subjectivity analysis classifies content in objective or subjective.
• subjective sentences ≠ +ve or –ve opinions
E.g., I think he came yesterday.
• Objective sentence ≠ no opinion
Imply –ve opinion: My phone broke in the second day.

14. APPLICATIONS :
1. Information Extraction
2. Product Review Mining
3. Review Classification
4. Prediction
5. Tracking Sentiments
6. Text Semantic Analysis
7. Text Summarization

15. Conclusion :
 Opinion mining is now a days very important concept in the field of
web mining. Because every thing we do in a real life Is related with
the opinion of someone else.
 Opinion or Sentiment analysis can be performed by using machine
learning techniques (Naïve bayes , SVM) .
 Classification of Sentiments are used for classifying the expressions on
various levels .
 Basically Opinion Mining concept is used for getting accuracy , from
which technique we are getting higher accuracy will be used.

16. References :
 B. Liu, “Sentiment Analysis and Subjectivity.”
 Opinion Mining and Sentiment Analysis, Foundations and Trends in Information
Retrieval, B. Pang and L. Lee, Vol. 2, Nos. 1–2 (2008) 1–135, 2008.
 http://en.wikipedia.org/wiki/subjectivity
 http://www.answers.com/sentiment
 https://people.cs.kuleuven.be>teaching
 www.facweb.iitkgp.ernet.in>my-opinion
 www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-
survey.html

17. THANK YOU……!!!!!!