This document provides an overview of opinion mining and sentiment analysis. It defines opinion mining as attempting to automatically determine human opinion from natural language text. It discusses some key applications, such as classifying reviews and understanding public opinion. The document also outlines some challenges, such as understanding context and differing domains. It then describes common models for sentiment analysis, including preparing data, analyzing reviews linguistically, and classifying sentiment using techniques like machine learning classifiers.

1. Opinion Mining

2. Outline
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Definition
Applications
Challenges
Model
Conclusion
References

3. Definition

Opinion mining (sentiment mining, opinion/sentiment
extraction) is the area of research that attempts to
make automatic systems to determine human opinion
from text written in natural language.

It seeks to identify the view point (s) underlying a
text span; an example application is classifying a
movie review as thumbs up or thumbs down.

4. Opinion mining is a new discipline which has
recently attracted increased attension within fields
such as Marketing,Recomandation systems and
financial market prediction.Although often
associated Emotional states from text,Opinion
Mining is an independent area related to Natural
Language Processing And Text mining that deals
with the Identification of opinionsAnd attitudes in
. Natural Language Text

5. 
Consider, for instance, the following scenario. A
major computer manufacturer, disappointed with
unexpectedly low sales, finds itself confronted with
this question:
Why aren't consumers buying our laptop?

6. 
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What other people think has always been an
important piece of information for most of us during
the decision-making process.
Opinion mining draws on computational linguistic,
information retrieval, text mining, natural language
processing, machine learning, statistics and predictive
analysis

7. 
1.
2.
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Two main types of textual information.
Facts
Opinions
Most current information processing technique (e.g.,
search engines) work with facts (assume they are
true)
Facts can be expressed with topic keywords

8. In real life, facts are important, but opinion also
plays a crucial role. A computer manufacturer,
disappointed with low sales, asks itself: Why aren’t
consumers buying our laptop? The Democratic
National Committee, disappointed with the last
election, wants to know on an on-going basis: What
is the reaction in the press, newsgroups, chat rooms,
and blogs to Bush’s latest policy decision?
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9. 
The main advantage is the speed
On average, humans process six articles
per hour against the machine’s throughput of 10 per
second

10. Applications
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recommendation systems
Summarization
Applications in Business
 marketing intelligence,
 product and service benchmarking and
improvement.
 To understand the voice of the customer as
expressed in everyday communications

11. Applications
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Politics
As is well known, opinions matter a
great deal in politics. Some work has focused on
understanding what voters are thinking

12. Challenges
The difficulty lies in the richness of the language that
human use.
Example:
1. This is a great camera.
2. A great amount of money was spent for
promoting this camera.
3. One might think this is a great camera. Well
think again, because.....
 a single keyword can be used to convey three
different opinions, +ve, neutral and -ve respectively.


13. Challenges
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In order to arrive at sensible conclusions, sentiment
analysis has to understand context. For example,
“fighting” and “disease” is negative in a war context
but positive in a medical one.
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Different mining for different domains.

14. sentiment analysis model

15. Data Preparation
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The data preparation step performs necessary data
preprocessing and cleaning on the dataset for the
subsequent analysis. Some commonly used
preprocessing steps include removing non-textual
contents and markup tags (for HTML pages), and
removing information about the reviews that are not
required for sentiment analysis, such as review dates
and reviewers’ names.

16. Review Analysis

The review analysis step analyzes the linguistic
features of reviews so that interesting information,
including opinions and/or product features, can be
identified.

This step often applies various computational
linguistics tasks to reviews first, and then extracts
opinions and product features from the processed
reviews.

17. Sentiment Classification
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There are two main techniques for sentiment
classification:

The symbolic technique uses manually crafted rules
and lexicons,
The machine learning approach uses unsupervised, or
supervised learning to construct a model from a large
training corpus.
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18. ?What
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Find relevant words, phrases, patterns that can be
used to express subjectivity
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Determine the polarity of subjective expressions

19. Words
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Adjectives
positive: honest important mature large patient
Ron Paul is the only honest man in Washington.

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Kitchell’s writing is unbelievably mature and is only likely to get
better.
To humour me my patient father agrees yet again to my choice of
film
negative: harmful hypocritical inefficient insecure
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It was a macabre and hypocritical circus.
Why are they being so inefficient ?

20. Words
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Verbs
positive: praise, love
 negative: blame, criticize
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Nouns
positive: pleasure, enjoyment
 negative: pain, criticism
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21. Phrases
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Phrases containing adjectives and adverbs
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positive: high intelligence, low cost
negative: little variation, many troubles

22. Machine Learning
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Studies showed that standard machine learning
techniques definitively outperform humanproduced baselines.
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To treat sentiment classification simply as a
special case of topic-based categorization
(with the two “topics” being positive sentiment
and negative sentiment)

23. Supervised Methods

In order to train a classifier for sentiment recognition
in text, classic supervised learning techniques (e.g.
Support Vector Machines, naive Bayes, Maximum
Entropy) can be used. A supervised approach entails
the use of a labelled training corpus to learn a certain
classification function. The method that in the
literature often yields the highest accuracy regards a
Support Vector Machine classifier

24. Suport Vector Machine

26. Unsupervised Learning
A clustering algorithm partitions the adjectives into two
subsets
+
slow
scenic
nice
terrible
handsome
painful
fun
expensive
comfortable

27. Conclusion
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An important field of study
New Field
Many applications
Almost no work in this area

28. References
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Pang, Bo and Lee, L. (2008). “Opinion Mining and
Sentiment Analysis”, Foundations and Trends R in,
Information Retrieval, Vol. 2, Nos. 1–2 (2008) 1–
135, ebook from
http://www.cs.cornell.edu/home/llee/omsa/omsa.pdf
Wiebe, J. Cardie, C. and Riloff, E. ( 2007).
“Manual and Automatic Subjectivity and Sentiment
Analysis” , Center for Extraction and
Summarization of Events and Opinions in Text.
University of Utah