1. University of Computer Studies, Mandalay
Ontology Based Comparative Sentence and Relation Mining For Sentiment Classification
Presented By
Myat Su Wai
Ph.D 4th Batch
University of Computer Studies, Mandalay

2. Abstract
Opinions are very important whenever one need to make a decision, and one want to hear other‘s opinion.
Opinion Mining is the mining of attitudes, opinions, and emotion and sentiment analysis classifies the polarity of opinions.
Ontology based comparative sentences and relation mining are studied for comparative opinions.
Naive Bayes classifier is also used for sentiment classification such as positive, negative and neutral

3. Introduction
Opinion mining and sentiment classification are not only technically challenging because of the need for natural language processing, but also very useful in practice
opinion mining can also provide valuable information for placing advertisements in Web pages
The Web has dramatically changed the way that people express their opinions.
In a page, if people express positive opinions or sentiments on a product, it may be a good idea to place an ad of the product.
And if people express negative opinions about the product, it is probably not wise to place an ad of the product
The Web has dramatically changed the way that people express their opinions.
They can now post reviews of products at merchant sites and express their views on almost anything in Internet forums, discussion groups, blogs, etc., which are commonly called the user generated content or user generated media.

4. Cont’d
They can now post reviews of products at merchant sites and express their views on almost anything in Internet social media
Opinion Mining is the mining of attitudes, opinions, and emotions automatically from text, speech, and database sources through Natural Language Processing (NLP).

5. Types of Opinions Regular opinion Comparative opinion
Features or aspects of entities or objects on which people have expressed their opinions
determines whether the opinions are positive or negative
Comparative opinion
Compare the object with some other objects
have different semantic meaning and syntactic forms

6. Sentiment Classification
Sentiment classifier classifies each sentence into one of the two classes, positive, negative and neutral.
The main application of sentiment classification is to give a quick determination of opinion on an object
In topic-based classification, topic related words are important
In sentiment classification, sentiment words that indicate positive or negative opinions are important
Positive means that document d expresses a positive opinion.
Negative means that document d expresses a negative opinion
The task is similar but also different from classic topic-based text classification, which classifies documents into predefined topic classes, e.g., politics, science, sports, etc

7. Comparative Sentence and Relation Mining
A sentence that expresses a relation based on similarities or differences of more than one object
Identifying comparative sentences comparative relations in text documents.
A comparison can be between two or more objects
Usually expressed using the comparative or superlative form of adjectives or adverbs.
A comparison can be between two or more objects, groups of objects, one object and the rest of the objects.

8. Types of Important Comparisons
Types of important comparisons can be classified into four main types.
Non-equal gradable comparisons
Equative comparisons
Superlative comparisons
Non-gradable comparisons
. For simplicity, from now on we use comparative sentences and gradable comparative sentences interchangeably.
Note that in a long comparative sentence, there can be multiple relations separated by delimiters such as commas “,” and conjunctions such as “and” and “but”.

9. Part-Of-Speech Tagging
It is useful to our subsequent discussion and also the proposed techniques
Common POS categories are: noun, verb, adjective, adverb, pronoun, preposition, conjunction and interjection
In part-of speech (POS) tagging, each word in review sentence is tagged with its part-of –speech.
After POS tagging, it is possible to retrieve nouns as product features and adjective as opinion words.
NN: Noun, NNP: Proper Noun, PRP: Pronoun, VBZ: Verb, present tense, 3rd person singular, JJR: Comparative Adjective, JJS: Superlative Adjective, RBR: Comparative Adverb, RBS: Superlative Adverb.
Although JJR, JJS, RBR, and RBS tags represent comparatives, many sentences containing such tags are not comparisons.
Many sentences that do not contain any of these tags may be comparisons.
Thus, we cannot solely use these tags to identify comparative sentences

10. Output of Standford POS Tagger

11. Ontology Based Approach to Sentiment Classification
Defined as the specialization of the conceptualization
Used in classification of opinions and in feature-based opinion mining.
Makes feature based opinion mining easier to conduct because of its graph structure..
Uses domain ontology to get domain related features
In phone ontology, “Phone” is a main class and it has subclasses and relations with them .
We use HasA relation and IsA relation between these class and subclasses.
And we also use other relations such as include and provide. We construct Mobile phone ontology by using Protégé 4.3 as shown in figure.

12. Creating Mobile Phone Ontology

13. Sentiment Polarity Classification by Using Naïve Bayes Classifier
Opinion orientation of each comparative review sentence is classified as positive, negative or neutral opinion.
For eg. Sumsaung is better than Huawei.
Above comparative sentence contains
opinion word ‘better’ which is comparative opinion word
two entities or features ‘Sumsaung’ and ‘Huawei’.

14. Cont’d Another example is “Huawei is not as good as Sumsaung.”
Without considering ‘not’, opinion word ‘good’ expresses the author prefer both Huawei and Sumsaung.
This sentence expresses the author prefer Sumsaung than Huawei because of negation word ’not’
Therefore, identification a list of negation words such as ‘no’, not’, but’ etc will be prepared.

15. Without the use of domain ontology Proposed Approach (With ontology)
Discussion
Without the use of domain ontology
Proposed Approach (With ontology)
Positive Sentiment
Accuracy: 72%
Recall : 90%
Accuracy: 80%
Negative Sentiment
Accuracy: 64%
Recall :
Accuracy: 76%
Recall : 85%
Ambiguous Sentiment
Accuracy: 65%
Neutral Sentiment
Accuracy: 60%
Accuracy: 75%

16. Conclusion
Opinion mining can be served in the field of Information search & Retrieval
Determining sentiments seems to be easier, determining comparative objects and their corresponding features is harder.
Naïve Bayes classifier is used to classify word of comparative sentences and relations from ontology.
According to study, ontology based approach is better in accuracy and recall than other approaches.

17. References
Bing Liu, Web Data Mining, Springer, Department of Computer Science, University of Illinois, Chicago, USA, I5th ed., pp , SBN Springer Berlin Heidelberg New York, 2007
Hanshi W, Xinhui N, Lizhen L, A Fuzzy Domain Sentiment Ontology based Opinion Mining Approach for Chinese Online Product Reviews, JOURNAL OF COMPUTERS, VOL. 8, NO. 9, SEPTEMBER 2013
Khin Phyu Phyu Shein and Thi Thi Soe Nyunt, “Sentiment Classification based on ontology and SVM classifier”, 2010 Second International Conference on Communication Software and Networks, IEEE, 2010, DOI /ICCSN
Wei Wang, TieJun Zhao, GuoDong Xin and YongDong Xu “Recognizing Comparative Sentences from Chinese Review Texts”, International Journal of Database Theory and Application Vol.7, No.5, pp.29-38,

18. Thank You!