Opinion mining of online customer reviews

Customer Opinions play a very crucial role in daily life. When we have to take a decision, opinions of other individuals are also considered. Now-a-days many of web users post their opinions for many products through blogs, review sites and social networking sites. Business organizations and corporate organizations are always eager to find consumer or individual views regarding their products, support and service. In e-commerce, online shopping and online tourism, its very crucial to analyse the good amount of social data present on the Web automatically therefore, its very important to create methods that automatically classify them. Opinion Mining sometimes called as Sentiment Classification is defined as mining and analysing of reviews, views, emotions and opinions automatically from text, big data and speech by means of various methods. In this thesis we are going to see how Apriori frequent item set mining algorithm can be used for mining reviews from online reviews those are posted by customers. Our main theme is to create a system for analysing opinions which implies judgement of different consumer products

Building Comparative Product Relation Maps by Mining Consumer Opinions on the Web

With the Web 2.0 paradigm, users play the active roles in producing Web contents at online forums, wiki, blogs, social networks, etc. Among these users contributed contents, many of them are opinions about products, services, or political issues. Accordingly, extracting the comparative relations about products or services by means of opinion mining techniques could generate significant business values. From the producers’ perspective, they could better understand the relative strength or weakness of their products, and hence developing better products to meet the consumers’ requirements. From the consumers’ perspective, they could exercise more informed purchasing decisions by comparing the various features of certain kind of products. The main contribution of this paper is the development of a novel Support Vector Machine (SVM) based comparative relation map generation method for automatic product features analysis based on the sheer volume of consumer opinions posted on the Web. The proposed method has been empirically evaluated based on the consumer opinions crawled from the Web recently. Our initial experimental results show that the performance of the proposed method is promising, and the precision can achieve 73.15%

Identifying Customer Preferences about Tourism Products Using an Aspect-based Opinion Mining Approach

AbstractIn this study we extend Bing Liu's aspect-based opinion mining technique to apply it to the tourism domain. Using this extension, we also offer an approach for considering a new alternative to discover consumer preferences about tourism products, particularly hotels and restaurants, using opinions available on the Web as reviews. An experiment is also conducted, using hotel and restaurant reviews obtained from TripAdvisor, to evaluate our proposals. Results showed that tourism product reviews available on web sites contain valuable information about customer preferences that can be extracted using an aspect-based opinion mining approach. The proposed approach proved to be very effective in determining the sentiment orientation of opinions, achieving a precision and recall of 90%. However, on average, the algorithms were only capable of extracting 35% of the explicit aspect expressions

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

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

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Mining social network data for personalisation and privacy concerns: A case study of Facebook’s Beacon

This is the post-print version of the final published paper that is available from the link below.The popular success of online social networking sites (SNS) such as Facebook is a hugely tempting resource of data mining for businesses engaged in personalised marketing. The use of personal information, willingly shared between online friends' networks intuitively appears to be a natural extension of current advertising strategies such as word-of-mouth and viral marketing. However, the use of SNS data for personalised marketing has provoked outrage amongst SNS users and radically highlighted the issue of privacy concern. This paper inverts the traditional approach to personalisation by conceptualising the limits of data mining in social networks using privacy concern as the guide. A qualitative investigation of 95 blogs containing 568 comments was collected during the failed launch of Beacon, a third party marketing initiative by Facebook. Thematic analysis resulted in the development of taxonomy of privacy concerns which offers a concrete means for online businesses to better understand SNS business landscape - especially with regard to the limits of the use and acceptance of personalised marketing in social networks