Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.

1. Nihar N Suryawanshi
I.T Grad at University of Pune

2.  Introduction
 Need of Sentiment Analysis
 Approach
 Implementation
 Applications
 Advantages
 Challenges
 Conclusion
 References
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3. The process of computationally identifying
and categorizing opinions expressed in a piece of text,
especially in order to determine whether the writer's
attitude towards a particular topic, product, etc. is
positive, negative, or neutral.
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4. Sentiment analysis is a type of natural
language processing for tracking the mood of the
public about a particular product or topic.
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5.  Rapid growth of available subjective text on the
internet
 Web 2.0
 To make decisions
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6.  User’s Opinions :
Sameer : It’s a great movie
(Positive statement)
Neha : Nah!! I didn’t like it
at all.
(Negative statement)
Mayur : I like it alot!!!!!!!!!
(Positive statement)
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8.  Deep learning
 Deep learning is an approach and an attitude to
learning, where the learner uses higher-order
cognitive skills.
 NLP
 Use semantics to understand the language.
 Uses SentiWordNet
 Machine Learning
 Don’t have to understand the meaning
 Uses classifiers such as Naïve Byes, SVM, etc.
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9. According to the image,
firstly gathering the data on
which we are going to
perform is done. Analyse it
and then select the points
which are useful in the data.
After that patterns are
identified resembling with
the extracted points for
getting the answers.
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11.  Businesses and Organizations
 Brand analysis or competitive
intelligence
 New product perception
 Product and Service
benchmark
 Market Forecasting
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12.  Individuals : Interested in other's opinions when…
 Purchasing a product or using a service
 Finding opinions on political topics ,movies,etc.
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13.  Social Media :
 Finding general opinion about recent hot
topics in town
 Online forum hotspots
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14.  A lower cost than traditional methods of getting
customer insight.
 A faster way of getting insight from customer data.
 The ability to act on customer suggestions.
 Identifies an organisation's Strengths, Weaknesses,
Opportunities & Threats (SWOT Analysis) .
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15.  As 80% of all data in a business consists of words, the
Sentiment Engine is an essential tool for making
sense of it all.
 More accurate and insightful customer perceptions
and feedback.
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16. • Semantic Classification:
Semantic classification means finding the
meaning of the text.
• Smiles:
The review or text may have use of smiles which
specifies mood towards writing. Processing smiles can
be tedious job.
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17. • Negation:
There are 3 types in it as follows:
1.Valence shifter
Ex:“I find the functionality of the new mobile less
practical”
2.Connectives
Ex:“Perhaps it is a great phone, but I fail to see
why”
3.Modals
Ex:“In theory, the phone should have worked even
under water”
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18.  Sentiment Analysis can be used for analyzing opinions
in blogs, articles, Product reviews, Social Media
websites, Movie-review websites where a third person
narrates his views.
It has many applications and it is important field to
study.
It has Strong commercial interest because Companies
want to know how their products are being perceived
and also Prospective consumers want to know what
existing users think.
It is also found that different types of features and
classification algorithms are combined in an efficient
way
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19. 1. "Case Study: Advanced Sentiment Analysis". Retrieved
18 October 2013.
2. Bing Liu (2010). "Sentiment Analysis and Subjectivity".
Handbook of Natural Language Processing, Second
Edition, (editors: N. Indurkhya and F. J. Damerau),
2010.
3. "Sentiment Analysis on Reddit". Retrieved 10 October
2014.
4. G.Vinodhini ,RM.Chandrasekaran .”Sentiment Analysis
and Opinion Mining: A Survey “,International Journal
of Advanced Research in Computer Science and
Software Engineering
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