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

Research Directions, Challenges and Issues in Opinion Mining

Rapid growth of Internet and availability of user reviews on the web for any product has provided a need for an effective system to analyze the web reviews. Such reviews are useful to some extent, promising both the customers and product manufacturers. For any popular product, the number of reviews can be in hundreds or even thousands. This creates difficulty for a customer to analyze them and make important decisions on whether to purchase the product or to not. Mining such product reviews or opinions is termed as opinion mining which is broadly classified into two main categories namely facts and opinions. Though there are several approaches for opinion mining, there remains a challenge to decide on the recommendation provided by the system. In this paper, we analyze the basics of opinion mining, challenges, pros & cons of past opinion mining systems and provide some directions for the future research work, focusing on the challenges and issues

Extracting Product Features from Online Consumer Reviews

Along with the exponential growth of user-generated content online comes the need of making sense of such content. Online consumer review is one type of user-generated content that has been more important. Thus, there is a demand for uncovering hidden patterns, unknown relationships and other useful information. The focal problem of this research is product feature extraction. Few existing studies has looked into detailed categorization of review features and explored how to adjust extraction methods by taking account of the characteristics of different categories of features. This paper begins with the introduction of a new scheme of feature classification and then introduces new extraction methods for each type of features separately. These methods were design to not only recognize new features but also filter irrelevant features. The experimental results show that our proposed methods outperform the state-of-the-art techniques

Suggestion Mining from Customer Reviews

The increasing online content has influenced users’ buying behavior. It has triggered a paradigm shift in marketing strategies,as the consumer is no longer swayed by marketers, instead relying on user comments for a particular product or service. Thispaper focuses on extracting information from feedbacks like suggestions and recommendation by the users that is oftenpresent along with the sentiment. While Sentiment Analysis looks at extraction of consumer sentiment, our focus is onextracting actionable feedback present in the text for use by different stakeholders like business analysts and the customer.Our focus is on mining the key suggestions present in text which would benefit the product developer. We present our resultsand observations in the paper