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Extracting Opinions from Reviews

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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Extracting Opinions from Reviews

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  1. Extracting Opinions from Reviews - AnuragKulkarni - ManishaMishra -RaaginiVenkatramani

  2. 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. • Identification of the opinion of the product. • Determining the polarity of the opinion. • Ranking the opinion based on their strength

  3. PROJECT SCHEDULING • AnuragKulkarni: AK • ManishaMishra: MM • RaaginiVenkatramani: RV

  4. PROJECT IMPLEMENTATION • Language Used: Java • Platform Used : Eclipse • Software for visualization: Tableau • Logic Used : The project uses the idea ofNaïve Bayes Classifier to classify the polarity of opinions.

  5. 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.

  6. VOCABULARY • 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

  7. IMPLEMENTATION 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)

  8. OUTPUT

  9. OBSERVATION1

  10. OBSERVATION 2

  11. OBSERVATION 3

  12. CONCLUSION • 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.

  13. 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 FeiXu for keeping himself available all the times to solve our doubts.

  14. BIBLIOGRAPHY AND REFERENCES BIBLIOGRAPHY: Introduction to Information Retrieval Christopher D. Manning, PrabhakarRaghavan, and HinrichSchütze. 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-FEEDBACK-useful-WORDS-and-PHRASES-for-BUYERS_W0QQugidZ10000000000733349

  15. THANK YOU!!

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