Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts. Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy. In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.

1. Opinion Mining

2. Outline
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
often
financial
associated
market prediction.Although
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
m ajor computer manufacturer, disappointed with
unexpectedly low sales, finds itself confronted with
this question:
Why aren't consumers buying our laptop?

6. What other people think has always been an
im portant 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. Two main types of textual information.
1. Facts
2. 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?

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

14. sentiment analysis model

15. Data Preparation
The data preparation step performs necessary data
pr eprocessing 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
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.

18. ?What
Find relevant words, phrases, patterns that can be
used to express subjectivity
Determine the polarity of subjective expressions

19. Words
Adjectives
positive: honest important mature large patient
Ron Paul is the only honest man in Washington.
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
It was a macabre and hypocritical circus.
Why are they being so inefficient ?

20. Words
Verbs
positive: praise, love
negative: blame, criticize
Nouns
positive: pleasure, enjoyment
negative: pain, criticism

21. Phrases
Phrases containing adjectives and adverbs
positive: high intelligence, low cost
negative: little variation, many troubles

22. Machine Learning
Studies showed that standard machine learning
techniques definitively outperform human-
produced baselines.
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
nice
handsome
terrible
painful
expensive
comfortable
fun
scenic
slow
+

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

28. References
Pang, Bo and Lee, L. (2008). “Opinion Mining and
Se ntiment 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