Transcript Extracting Opinions from Reviews.ppt

EXTRACTING OPINIONS FROM
REVIEWS
- Anurag Kulkarni
- Manisha Mishra
-Raagini Venkatramani
SCOPE OF THE PROJECT
The project is primarily concerned with the
development of an application which extracts
opinions from reviews (available online) of
various products viz.
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Identification of the opinion of the product.
Determining the polarity of the opinion.
Ranking the opinion based on their strength
PROJECT SCHEDULING
WORK TASK
PLANNED START PLANNED
COMPLETE
ASSIGNED
PERSON
1. Identify needs and
benefits
SEPTEMBER
SEPTEMBER
AK,MM,RV
2. Collection of
reviews.
OCTOBER
OCTOBER
RV
3. Creation of
Vocabulary and
dataset.
OCTOBER
OCTOBER
RV,MM,AK
4. Design and
Implementation.
NOVEMBER
NOVEMBER
AK,MM,RV
5. Creation of
visualizations.
DECEMBER
DECEMBER
MM, AK
Anurag Kulkarni: AK
Manisha Mishra: MM
Raagini Venkatramani: RV
PROJECT IMPLEMENTATION
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Language Used : Java
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Platform Used : Eclipse
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Software for visualization : Tableau
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Logic Used :
The project uses the idea of Naïve Bayes
Classifier to classify the polarity of opinions.
REVIEWS
The project uses a collection of 40 reviews
averagely for each product.
 The reviews are stored in a text file.
 They are gathered from sites
www.amazon.com
 The project makes use of the reviews of the
following two categories:
Camera
Hard-Disk
Each item has five products in it.
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VOCABULARY
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A vocabulary is made manually on the basis of the
reviews collected.
Vocabulary contains the following :
1). Positive Words
2). Positive Phrases
3). Negative Words
4). Negative Phrases
Example of Positive Words Vocabulary :
1). flawless
2). great
3). simple
IMPLEMENTATION
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Algorithm
Fetch review from the directory.
Fragment the reviews in the sentences.
Assign weight to the sentences based on the
vocabulary. (Vocabulary contains positive words and
phrases ,negative words and phrases)
Sum up the weight of the sentences to get weight of
the review.
Sum up weight of the reviews to get overall product
score.
Rate of the product = number of reviews with positive
score / number of reviews with
negative scores + log(number of
neutral reviews)
OUTPUT
OBSERVATION 1
OBSERVATION 2
OBSERVATION 3
CONCLUSION
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We have successfully analyzed reviews in the
following manner:
Identified opinions of various product.
Determined the polarity of the opinion.
Ranked opinions based on their strength.
ACKNOWLEDGEMENTS
We would like to thank Dr. Wengsheng Wu for
guiding us in the project and making himself
available all the times for clearing our doubts.
 Also we would like to thank our T.A Fei Xu for
keeping himself available all the times to solve
our doubts.
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BIBLIOGRAPHY AND REFERENCES
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BIBLIOGRAPHY:
Introduction to Information Retrieval
Christopher D. Manning, Prabhakar Raghavan,
and Hinrich Schütze. Cambridge University
Press, 2008.
REFERENCES:
Extracting Product Features and Opinions from
Reviews , Ana-Maria Popescu, Oren Etzioni,
Proceedings of HLT-EMNLP, 2005
http://reviews.ebay.com/POSITIVE-FEEDBACKuseful-WORDS-and-PHRASES-forBUYERS_W0QQugidZ10000000000733349
THANK YOU!!