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

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

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.

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).

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

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

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.

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”.

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

Output of Standford POS Tagger

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.

Creating Mobile Phone Ontology

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’.

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.

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%

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.

References Bing Liu, Web Data Mining, Springer, Department of Computer Science, University of Illinois, Chicago, USA, I5th ed., pp. 375-410, SBN-10 3-540-37881-2 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 10.1109/ICCSN.2010.35. 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, http://dx.doi.org/10.14257/ijdta.2014.7.5.03

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