Product reputation model : an opinion mining based approach

Product rating systems are very popular on the web, and users are increasingly depending on the overall product ratings provided by websites to make purchase decisions or to compare various products. Currently most of these systems directly depend on users��� ratings and aggregate the ratings using simple aggregating methods such as mean or median [1]. In fact, many websites also allow users to express their opinions in the form of textual product reviews. In this paper, we propose a new product reputation model that uses opinion mining techniques in order to extract sentiments about product���s features, and then provide a method to generate a more realistic reputation value for every feature of the product and the product itself. We considered the strength of the opinion rather than its orientation only. We do not treat all product features equally when we calculate the overall product reputation, as some features are more important to customers than others, and consequently have more impact on customers buying decisions. Our method provides helpful details about the product features for customers rather than only representing reputation as a number only.

Product feature taxonomy learning based on user reviews

In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that our proposed approach is able to capture the product features and relations effectively.

Quality-aware review selection based on product feature taxonomy

As of today, user-generated information such as online reviews has become increasingly significant for customers in decision making process. Meanwhile, as the volume of online reviews proliferates, there is an insistent demand to help the users tackle the information overload problem. In order to extract useful information from overwhelming reviews, considerable work has been proposed such as review summarization and review selection. Particularly, to avoid the redundant information, researchers attempt to select a small set of reviews to represent the entire review corpus by preserving its statistical properties (e.g., opinion distribution). However, one significant drawback of the existing works is that they only measure the utility of the extracted reviews as a whole without considering the quality of each individual review. As a result, the set of chosen reviews may consist of low-quality ones even its statistical property is close to that of the original review corpus, which is not preferred by the users. In this paper, we proposed a review selection method which takes review quality into consideration during the selection process. Specifically, we examine the relationships between product features based upon a domain ontology to capture the review characteristics based on which to select reviews that have good quality and preserve the opinion distribution as well. Our experimental results based on real world review datasets demonstrate that our proposed approach is feasible and able to improve the performance of the review selection effectively.

Generating product feature hierarchy from product reviews

User generated information such as product reviews have been booming due to the advent of web 2.0. In particular, rich information associated with reviewed products has been buried in such big data. In order to facilitate identifying useful information from product (e.g., cameras) reviews, opinion mining has been proposed and widely used in recent years. In detail, as the most critical step of opinion mining, feature extraction aims to extract significant product features from review texts. However, most existing approaches only find individual features rather than identifying the hierarchical relationships between the product features. In this paper, we propose an approach which finds both features and feature relationships, structured as a feature hierarchy which is referred to as feature taxonomy in the remainder of the paper. Specifically, by making use of frequent patterns and association rules, we construct the feature taxonomy to profile the product at multiple levels instead of single level, which provides more detailed information about the product. The experiment which has been conducted based upon some real world review datasets shows that our proposed method is capable of identifying product features and relations effectively.

Online nail art application : proceeding of the project development

Online Nail Artist (ONA) project aims to create a web-based application for nail salon customers. The application will help customers to customize their hands virtually and find suitable nail colors. The main research question is to reconfigure user experience in relation to product service in terms of customization of user needs. As results, the key function of the application will be to customize a virtual hand image by selecting a matched skin tone, a nail length, and a nail shape in accordance with their hands. The objectives of the project proceeding are to 1) identify customers��� experience in relation to the product features through preliminary research on existing products; 2) create a conceptual framework of the project development in order to reflect the user experience identified; and 3) present a mock up which include key features of the ONA for the future development.

Just give me the facts? literalism versus symbolism in B2B advertising

For decades the prevailing idea in B2B marketing has been that buyers are motivated by product/service specifications. Sellers are put on approved supplier lists, invited to respond to RFPs, and are selected on the basis of superior products, at the right price, delivered on time. The history of B2B advertising is filled with the advice ���provide product specifications��� and your advertising will be noticed, lead to sales inquiries, and eventually result in higher sales. Advertising filled with abstractions might work in the B2C market, but the B2B marketplace is about being literal. What we know about advertising ��� and particularly the message component of advertising ��� is based on a combination of experience, unproven ideas and a bit of social science. Over the years, advertising guidelines produced by the predecessors of BMA (National Industrial Advertising Association, Association of Industrial Advertising, and the Business/Professional Advertising Association) stressed emphasizing product features and tangible benefits. The major publishers of B2B magazines, e.g., McGraw-Hill, Penton Publishing, et al. had similar recommendations. Also, B2B marketing books recommend advertising that focuses on specific product features (Kotler and Pfoertsch, 2006; Lamons, 2005). In more recent times, abstraction in advertising messages has penetrated the B2B marketplace. Even though such advertising legends as David Ogilvy (1963, 1985) frequently recommended advertising based on hard-core information, we���ve seen the growing use of emotional appeals, including humor, fear, parental affection, etc. Beyond the use of emotion, marketers attempt to build a stronger connection between their brands and buyers through the use of abstraction and symbolism. Below are two examples of B2B advertisements ��� Figure 1A is high in literalism and Figure 1B is high in symbolism. Which approach ��� a ���left-brain��� (literal) or ���right brain��� (symbolic) is more effective in B2B advertising? Are the advertising message creation guidelines from the history of B2B advertising accurate? Are the foundations of B2B message creation (experience and unproven ideas) sound?

From valuing design to designing value

This paper presents findings of an embedded action research project within a small to medium sized enterprise (SME). Through the implementation of design-led innovation processes, this research aims to identify the changes experienced in the participating company during a shift in the perspective of design from a product focus towards a strategic focus. Staff interviews and a reflective journal were used as methods to collect data from a range of design interventions that were facilitated throughout the engagement. A shift in perspective of design was evident through three cultural changes within the firm. First, the perceived outcome focus of design became increasingly long-term. Second, the value of design outcomes became less directed towards current projects, and more directed towards future possibilities. Finally, the perceived tangibility of design outcomes shifted from tangible to intangible. For example, design activities which produced customer insights, rather than product features, became seen as beneficial to the firm. These three components are proposed as cultural stepping stones which describe how a company transitions from an exclusively product-focused perspective and utilisation of design towards design as a company based process. Implications of this research provide considerations for designers who are attempting to facilitate a similar transformation within a business in the future.

Social networking : investigating the features of Facebook application

Research on social networking sites like Facebook is emerging but sparse. The exploratory study investigates the value users derive from self-described ���cool��� Facebook applications, and explores the features that either encourage or discourage users to recommend application to their friends. Thus the concepts of value and cool are explored in a social networking setting. Our qualitative data shows that consumers derive a combination of functional value along with either social or emotional value from the applications. Female Facebook users indicated self-expression as important, while mates then to use Facebook application to socially compete. Three broad categories emerged for application features; symmetrical features can both encourage or discourage recommendation, asymmetrical features one encourage or discourage but not both, and polar features where different levels of the same feature encourage or discourage. Recommending or not recommending an application tends to be the result of a combination of features rather than one feature in isolation.

Reasoning about component similarity in building product models from the construction perspective

Identifying the design features that impact construction is essential to developing cost effective and constructible designs. The similarity of building components is a critical design feature that affects method selection, productivity, and ultimately construction cost and schedule performance. However, there is limited understanding of what constitutes similarity in the design of building components and limited computer-based support to identify this feature in a building product model. This paper contributes a feature-based framework for representing and reasoning about component similarity that builds on ontological modelling, model-based reasoning and cluster analysis techniques. It describes the ontology we developed to characterize component similarity in terms of the component attributes, the direction, and the degree of variation. It also describes the generic reasoning process we formalized to identify component similarity in a standard product model based on practitioners' varied preferences. The generic reasoning process evaluates the geometric, topological, and symbolic similarities between components, creates groupings of similar components, and quantifies the degree of similarity. We implemented this reasoning process in a prototype cost estimating application, which creates and maintains cost estimates based on a building product model. Validation studies of the prototype system provide evidence that the framework is general and enables a more accurate and efficient cost estimating process.

Capturing gazelles : features of high potential firms and new venture growth

This paper investigates the characteristics of ventures which have the potential to reach high growth and compares this with ���everyday��� new ventures. Findings of interest in this paper include: ��� HP firms are characterised by higher human capital, are more likely to have a team of founders, are more likely to be product based. ��� HP firms are more likely to achieve more extreme levels of growth (both positive and negative). ��� HP ventures that make a loss are more likely to do so early in the venture process. Those that do hold on show that there can higher levels of loss made later on in firm development. HP firms have higher resource needs, in terms of seeking external finance, but are no more likely to receive external finance than regular firms.

Ontology-based product's reputation model

Different reputation models are used in the web in order to generate reputation values for products using uses' review data. Most of the current reputation models use review ratings and neglect users' textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And that's why the use of users' textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for users' extracted opinions about products' features from users' text. Our model uses features ontology in order to define general features and sub-features of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.

A review selection method using product feature taxonomy

As of today, online reviews have become more and more important in decision making process. In recent years, the problem of identifying useful reviews for users has attracted significant attentions. For instance, in order to select reviews that focus on a particular feature, researchers proposed a method which extracts all associated words of this feature as the relevant information to evaluate and find appropriate reviews. However, the extraction of associated words is not that accurate due to the noise in free review text, and this affects the overall performance negatively. In this paper, we propose a method to select reviews according to a given feature by using a review model generated based upon a domain ontology called product feature taxonomy. The proposed review model provides relevant information about the hierarchical relationships of the features in the review which captures the review characteristics accurately. Our experiment results based on real world review dataset show that our approach is able to improve the review selection performance according to the given criteria effectively.

Life is too short to RTFM : how users relate to documentation and excess features in consumer products

This paper addresses two common problems that users of various products and interfaces encounter��� over-featured interfaces and product documentation. Over-featured interfaces are seen as a problem as they can confuse and over-complicate everyday interactions. Researchers also often claim that users do not read product documentation, although they are often exhorted to ���RTFM���(read the field manual).We conducted two sets of studies with users which looked at the issues of both manuals and excess features with common domestic and personal products. The quantitative set was a series of questionnaires administered to 170 people over 7 years. The qualitative set consisted of two 6-month longitudinal studies based on diaries and interviews with a total of 15 participants. We found that manuals are not read by the majority of people, and most do not use all the features of the products that they own and use regularly. Men are more likely to do both than women, and younger people are less likely to use manuals than middle-aged and older ones. More educated people are also less likely to read manuals. Over-featuring and being forced to consult manuals also appears to cause negative emotional experiences. Implications of these findings are discussed.

The Behavior of Hydroxyl Units of Synthetic Goethite and its Dehydroxylated Product Hematite

The behavior of the hydroxyl units of synthetic goethite and its dehydroxylated product hematite was characterized using a combination of Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) during the thermal transformation over a temperature range of 180-270 degrees C. Hematite was detected at temperatures above 200 degrees C by XRD while goethite was not observed above 230 degrees C. Five intense OH vibrations at 3212-3194, 1687-1674, 1643-1640, 888-884 and 800-798 cm(-1), and a H2O vibration at 3450-3445 cm(-1) were observed for goethite. The intensity of hydroxyl stretching and bending vibrations decreased with the extent of dehydroxylation of goethite. Infrared absorption bands clearly show the phase transformation between goethite and hematite: in particular. the migration of excess hydroxyl units from goethite to hematite. Two bands at 536-533 and 454-452 cm(-1) are the low wavenumber vibrations of Fe-O in the hematite structure. Band component analysis data of FTIR spectra support the fact that the hydroxyl units mainly affect the a plane in goethite and the equivalent c plane in hematite.