software quality in use characteristic mining from customer reviews warit leopairote, athasit...
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Software Quality in Use Characteristic Mining from
Customer ReviewsWarit Leopairote, Athasit Surarerks, Nakornthi
p PrompoonDepartment of Computer Engineering, Faculty
of Engineering
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Outline
• I. INTRODUCTION
• II. BACKGROUND• A. Software Quality• B. Ontology Based Opinion Mining
• III. EVALUATION
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INTRODUCTION
• Because of time and resource constraints, customers may not view all products offered or available at an e-commerce website
• opinion mining that extracts, analyses and aggregates information.
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INTRODUCTION
• This approach focuses on analyzing the sentimental sentence that results in a positive or negative polarity judgment.
• To classify sentimental sentence to product attributes, ontology mapping is used
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INTRODUCTION
• One of the widely accepted among software engineers in software quality model is ISO 9126 which presents one part of quality model, named quality in use
• effectiveness, productivity, safety and satisfaction characteristic
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Outline
• I. INTRODUCTION
• II. BACKGROUND• A. Software Quality• B. Ontology Based Opinion Mining
• III. EVALUATION
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A. Software Quality
• Our research focuses on ISO 9126 presented by the International Organization for Standardization
• This model contains two parts. The first part is internal and external quality.
• funcionality, reliability, usability, effeciency, maintainability and portability
• The second part is the quality in use.• effectiveness, productivity, safety and satisfaction
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A. Software Quality
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Outline
• I. INTRODUCTION
• II. BACKGROUND• A. Software Quality• B. Ontology Based Opinion Mining
• III. EVALUATION
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B. Ontology Based Opinion Mining
• Technically, sentiment analysis and opinion mining approaches are used to extract and analyze information from product reviews.
• Ontology based opinion mining is a method for opinion mining. It can also be used to simplify such various aspects into polarity.
• ontology mining can be divided into two main parts, ontology mapping and polarity mining
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B. Ontology Based Opinion Mining
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B. Ontology Based Opinion Mining
• Ontology mapping part consists of two processes: ontology definition and ontology mapping.
• Ontology definition may be constructed based on product attribute, feature and quality.
• Ontology mapping aims to match a sentence or a section of a customer review to terminology defined in ontology
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B. Ontology Based Opinion Mining
• In polarity mining part, many works on sentiment analysis classifies documents by their overall sentiment
• To construct classifier from machine learning approach
• A. Data Preparation Phase• B. Classifier Construction Phase• C. Reviews Analysis Phase• D. Quality in Use Score Calculation Phase
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B. Ontology Based Opinion Mining
• A. Data Preparation Phase• Tokens of sentence• If-tagging• Past-tense-tagging• No-or-not-tagging
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B. Ontology Based Opinion Mining
• B. Classifier Construction Phase• 1) Ontology construction part:• WordNet3.0
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B. Ontology Based Opinion Mining
• 2) Rule construction part• Two rule-based classifiers are created in this part• Sentences that tagged with “if”, “past tense” or “no
or not” are filtered out.• Text-Miner Software Kit (TMSK) and the Rule Indu
ction Kit(RIKTEXT)
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B. Ontology Based Opinion Mining
• C. Reviews Analysis Phase• 1) Mapping each sentence onto quality model by
ontology• 2) Classifying each sentence into positive or
negative sentence categorized by two rule-based classifiers
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B. Ontology Based Opinion Mining
• D. Quality in Use Score Calculation Phase• 1) Calculating quality in use score in each review
• 2) Calculating quality in use score of software
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III. EVALUATION
• A. Data Collection and Preparation• 500 reviews from 10 software. 3,002 sentences in
500 reviews are denoted as positive, neutral or negative opinions
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III. EVALUATION
• B. Relation between quality in use score and rating star
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III. EVALUATION
• C. Quality in use characteristic ontology mapping
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III. EVALUATION
• D. Polarity sentence classification
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