Towards a Modular Ontology for Cloud Consumer Review Mining

Nowadays, online consumer reviews are used to enhance the effectiveness of finding useful product information that impacts the consumers’ decision-making process. Many studies have been proposed to analyze these reviews for many purposes, such as opinion-based recommendation, spam review detection, opinion leader analysis, etc. A standard model that presents the different aspects of online review (review, product/service, user) is needed to facilitate the review analysis task. This research suggests SOPA, a modular ontology for cloud Service OPinion Analysis. SOPA represents the content of a product/service and its related opinions extracted from the online reviews written in a specific context. The SOPA is evaluated and validated using cloud consumer reviews from social media and using quality metrics. The experiments revealed that the SOPA-related modules exhibit a high cohesion and a low coupling, besides their usefulness and applicability in real use case studies

Extracting product development intelligence from web reviews

Product development managers are constantly challenged to learn what the consumer product experience really is, and to learn specifically how the product is performing in the field. Traditionally, they have utilized methods such as prototype testing, customer quality monitoring instruments, field testing methods with sample customers, and independent assessment companies. These methods are limited in that (i) the number of customer evaluations is small, and (ii) the methods are driven by a restrictive structured format. Today the web has created a new source of product intelligence; these are unsolicited reviews from actual product users that are posted across hundreds of websites. The basic hypothesis of this research is that web reviews contain significant amount of information that is of value to the product design community. This research developed the DFOC (Design - Feature - Opinion - Cause Relationship) method for integrating the evaluation of unstructured web reviews into the structured product design process. The key data element in this research is a Web review and its associated opinion polarity (positive, negative, or neutral). Hundreds of Web reviews are collected to form a review database representing a population of customers. The DFOC method (a) identifies a set of design features that are of interest to the product design community, (b) mines the Web review database to identify which features are of significance to customer evaluations, (c) extracts and estimates the sentiment or opinion of the set of significant features, and (d) identifies the likely cause of the customer opinion. To support the DFOC method we develop an association rule based opinion mining procedure for capturing and extracting noun-verb-adjective relationships in the Web review database. This procedure exploits existing opinion mining methods to deconstruct the Web reviews and capture feature-opinion pair polarity. A Design Level Information Quality (DLIQ) measure which evaluates three components (a) Content (b) Complexity and (c) Relevancy is introduced. DLIQ is indicative of the content, complexity and relevancy of the design contextual information that can be extracted from an analysis of Web reviews for a given product. Application of this measure confirms the hypothesis that significant levels of quality design information can be efficiently extracted from Web reviews for a wide variety of product types. Application of the DFOC method and the DLIQ measure to a wide variety of product classes (electronic, automobile, service domain) is demonstrated. Specifically Web review databases for ten products/services are created from real data. Validation occurs by analyzing and presenting the extracted product design information. Examples of extracted features and feature-cause associations for negative polarity opinions are shown along with the observed significance

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

Implicit Sentiment Identification using Aspect based Opinion Mining

Opinion mining or sentiment analysis is the computational study of opinions or emotions towards aspects or things. The aspects are nothing but attributes or components of the individuals, events, topics, products and organizations. Opinion mining has been an active research area in Web mining and Natural Language Processing (NLP) in recent years. With the explosive growth of E-commerce, there are millions of product options available and people tend to review the viewpoint of others before buying a product. An aspect-based opinion mining approach helps in analyzing opinions about product features and attributes. This project is based on extracting aspects and related customer sentiments on tourism domain. This offers an approach to discover consumer preferences about tourism products and services using statistical opinion mining. The proposed system tries to extract both explicit aspects as well as implicit aspects from customer reviews. It thus increases the sentiment orientation of opinion. Most of the researches were based on explicit opinions of customers. This system tries to retrieve implicit sentiments. Due to the growing availability of unstructured reviews, the proposed system gives a summarized form of the information that is obtained from the reviews in order to furnish customers with pin point or crisp results. DOI: 10.17762/ijritcc2321-8169.16049

Sentiment Analysis of Twitter Data Using Naive Bayes Algorithm

Sentiment analysis the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral. Now a days the growth of social websites, blogging services and electronic media con-tributes huge amount of user give messages such as customer reviews, comments and opinions. Sentiment Analysis is an important term referred to collect information in a source by using NLP, computational linguistics and text analysis and to make decision by subjective information extracting and analyzing opinion, identifying positive and negative reviews measuring how positively and negatively an entity (public ,organization, product) is involved. Sentiment analysis is the area of study to analyze people’s reviews, emotion, attitudes and emotion from written languages. We concentrate on field of different opinion classification techniques, performed on any data set. Now a days most popular approaches are Bag of words and feature extraction used by researchers to deal with sentiment analysis i.e used by politician, news groups, manufactures organization, movies, products etc