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A main advantage of app stores is that they aggregate important information created by both developers and users. In the app store product pages, developers usually describe and maintain the features of their apps. In the app reviews, users comment these features. Recent studies focused on mining app features either as described by developers or as reviewed by users. However, extracting and matching...
Hotel reviews posted on accommodation reservation websites are thought to be valuable information for selecting hotel accommodations and also expected to be used for marketing. Since hotel reviews are various in their expressions, it was necessary to make a thesaurus to obtain useful feature representations. Preparing a thesaurus, however, has problems such that it is laborious and requires occasional...
A rapid development of E-Commerce platforms has allowed retailers to introduce online product recommendations to persuade consumers purchase decisions. Recommendations system in E-Commerce can be implemented through development of opinion review or feedback system. The visibility of opinion review as a persuasive communication tool in recommendation context has been proven as an important role in...
Credit scoring is an important process in every financial institution and bank. Its high accuracy in classifying customers helps decrease the credit risk and increase reliability and profit. In this paper, we propose a binary classification approach that can classify customers who apply for loans. A statistical technique called Stepwise Regression (SR) is used as a pre-process to select important...
When looking for a restaurant online, user uploaded photos often give people an immediate and tangible impression about a restaurant. Due to their informativeness, such user contributed photos are leveraged by restaurant review websites to provide their users an intuitive and effective search experience. In this paper, we present a novel approach to inferring restaurant types or styles (ambiance,...
Statistical models are commonly fit to bulk datasets, and they are applied in quasi real-time to previously unseen data. Challenges lie not only in fitting these models to data, but also in keeping track of their development and deployment process. It is common practice to re-engineer data pre-processing functions that were created during model development in order to build a version for deployment...
Feature-based opinion mining for product review is the field of study that analyzes user's attitude towards product attributes, which has been witnessed a booming interest in the last one and half decades, due to its importance to business and society as a whole. This paper proposed a POS patterns matching method to identify feature words, opinion bearing words, as well as negative words based on...
In recent years, with the gradual development of mobile Internet technology, the number of mobile applications increases dramatically. Users facing numerous mobile applications are often caught off guard. It is necessary to automatically classify the applications according to the applications' information, so as to recommend appropriate applications to users. However, the text information directly...
With the increase in the number of user reviews on user review sites, useful tools for extracting good and bad points of services so that users can easily and intuitively understand the quality of the services are required. If the annotations are selected from the pre-defined list, there can always be missing keywords. Supervised annotation approaches would suffer from the same problem. In this paper,...
A growing demand for individualized products results in an increasing number of product variations. A first step to harmonize these variations is to identify similarities between products based on their technical properties.
According to the application requirements of the Water Resources Data Centre (WRDC), a scene analysis model is proposed for organizing and applying the objective information under the condition of big data. This model focuses on the generation and application of the digital scene, supporting big data processing, and establishing a new application mode of WRDC. This new model has the ability to combine...
User reported experiences and opinions are used by peers to make decisions about where to go and what to buy. Unfortunately, not all users or opinions are honest. Many opinions are fabricated and may be submitted by automated systems or by people who are recruited by businesses and search engine optimizers to write good reviews. Such reviews and ratings are called spam reviews. These are misleading...
User online shopping preference mining is the key point on user found, e-commerce marketing and user personalized recommendation. A method for Online shopping preference analysis based on MapReduce is proposed in this paper. The campus network traffic is analyzed using MapReduce model, in which the features of user online shopping behavior are extracted by four MapReduce jobs using deep packet inspection...
This research aims to automate the process of gathering online, end user reviews for any given product or service and analyzing those reviews in terms of the sentiments expressed about specific features. This involves the filtering of irrelevant and unhelpful reviews, quantification of the sentiments of thousands of (useful) reviews. And finally, providing the end user (business/manufacturer) summarized...
Maintenance of unused features leads to unnecessary costs. Therefore, identifying unused features can help product owners to prioritize maintenance efforts. We present a tool that employs dynamic analyses and text mining techniques to identify use case documents describing unused features to approximate unnecessary features. We report on a preliminary study of an industrial business information system...
Human memory often fails. People are frequently beset with questions like “Who is that person? I think I met him in Tokyo last year.” Existing memory aid tools cannot well support the recall of names effectively. This paper explores the memory recall enhancement issue from the perspective of memory cue extraction and associative search, and proposes a generic methodology to extract memory cues from...
Recently, eye-tracking has been widely applied in a wide spectrum of fields for both academic researches and Business. In this study, we concentrate on the analysis of instant (and often subconscious) information generated from interactions between an individual and devices, such as a PC, laptop, and mobile phone. We present the experiment design to capture and extract the viewing patterns in Twitter...
Automatic information extraction from scanned business documents is especially valuable in the application domain of document archiving. But current systems for automated document processing still require a lot of configuration work that can only be done by experienced users or administrators. We present an approach for information extraction which purely builds on end-user provided training examples...
A user's location information is commonly used in diverse mobile services, yet providing the actual name or semantic meaning of a place is challenging. Previous works required manual user interventions for place naming, such as searching by additional keywords and/or selecting place in a list. We believe that applying mobile sensing techniques to this problem can greatly reduce user intervention....
Fraud detection is a critical problem affecting large financial companies that has increased due to the growth in credit card transactions. This paper presents a new method for automatic detection of frauds in credit card transactions based on non-linear signal processing. The proposed method consists of the following stages: feature extraction, training and classification, decision fusion, and result...
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