3. Introduction
● The recent burst in the web usage has contributed to
the growth of number of various online reviews.
● Most of the reviews are objective where as some are
context sensitive and subjective.
● All these reviews contain mixture of negative, positive
or neutral comments.
● In today’s world people tend to go through these
online reviews before going ahead with any activities
like shopping, purchasing, and reading a book etc …
● This research aims on assisting the people by
developing an ontology for various online book reviews.
4. Introduction ...
● Ontology is a formal and explicit domain specific
reference model.
● Ontology reference model can be used for defining
set of concepts along with the relationship among
them.
● Therefore this nature provides an efficient way of
performing opinion mining on book reviews.
5. Introduction ...
● General opinion mining's are more
focused on
1. Context-free sentiment classification
2. Large number of manually annotated
training examples.
● This project focuses on context-sensitive opinion
mining system.
6. Contents of Book Review
● Description.
● Narration.
● Exposition.
● Argument.
● State of Knowledge.
● Content Description.
● Subject Area.
8. Ontology Development
● Ontology development is based on the
domain ( Domain Ontology ).
● Task of ontology construction is divided in
to two.
1. Select the relevant sentences including
conceptions.
2. Extract the conceptions from those
sentences.
12. Feature Identification
● This process is used for feature
identification of ontology terminologies.
● Extraction of the related sentence which
contain ontology terminologies.
● Those sentences can be used for feature
extraction.
14. Polarity Identification
● Initially two well known approaches are
considered.
❖ SentiWordNet
❖ WordNet-Affect
● Eventually we decided to use WordNet-
Affect.
15. WordNet-Affect …
● Emotional causes can be calculated in
two different ways.
❖ Direct Affective Words.
❖ Indirect Affective Words.
● Affective weight is calculated based on
semantic similarity mechanism which
acquires from large corpus of texts.
● Semantic Affinity for each emotion is
returned.
18. Sentiment Analysis
● Using the final lists of positive,
negative and neutral words or phrases,
opinion orientation expressed on each
feature can be analyzed.
● Hierarchy structure is used to calculate the
opinion of high level concept.
20. Conclusion
● This research attempts to create an
ontology for the book domain to perform
opinion mining.
● During the opinion mining process, polarity
of the word is being identified by using
WordNet-Affect.
22. References
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23. References ...
● Kaji, N., Kitsuregawa, M.: Automatic construction of
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