16,051 research outputs found
What attracts vehicle consumersâ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?
Purpose:
The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.
Design/methodology/approach:
A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel NaĂŻve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.
Findings:
The big data analytics argue that âcost-effectivenessâ characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.
Research limitations/implications:
The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.
Originality/value:
Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
Synthesis of Attributed Feature Models From Product Descriptions: Foundations
Feature modeling is a widely used formalism to characterize a set of products
(also called configurations). As a manual elaboration is a long and arduous
task, numerous techniques have been proposed to reverse engineer feature models
from various kinds of artefacts. But none of them synthesize feature attributes
(or constraints over attributes) despite the practical relevance of attributes
for documenting the different values across a range of products. In this
report, we develop an algorithm for synthesizing attributed feature models
given a set of product descriptions. We present sound, complete, and
parametrizable techniques for computing all possible hierarchies, feature
groups, placements of feature attributes, domain values, and constraints. We
perform a complexity analysis w.r.t. number of features, attributes,
configurations, and domain size. We also evaluate the scalability of our
synthesis procedure using randomized configuration matrices. This report is a
first step that aims to describe the foundations for synthesizing attributed
feature models
Linking objective and subjective modeling in engineering design through arc-elastic dominance
Engineering design in mechanics is a complex activity taking into account both objective modeling processes derived from physical analysis and designersâ subjective reasoning. This paper introduces arc-elastic dominance as a suitable concept for ranking design solutions according to a combination of objective and subjective models. Objective models lead to the aggregation of information derived from physics, economics or eco-environmental analysis into a performance indicator. Subjective models result in a confidence indicator for the solutionsâ feasibility. Arc-elastic dominant design solutions achieve an optimal compromise between gain in performance and degradation in confidence. Due to the definition of arc-elasticity, this compromise value is expressive and easy for designers to interpret despite the difference in the nature of the objective and subjective models. From the investigation of arc-elasticity mathematical properties, a filtering algorithm of Pareto-efficient solutions is proposed and illustrated through a design knowledge modeling framework. This framework notably takes into account Harringtonâs desirability functions and Derringerâs aggregation method. It is carried out through the re-design of a geothermal air conditioning system
Automatic Document Summarization Using Knowledge Based System
This dissertation describes a knowledge-based system to create abstractive summaries of documents by generalizing new concepts, detecting main topics and creating new sentences. The proposed system is built on the Cyc development platform that consists of the worldâs largest knowledge base and one of the most powerful inference engines. The system is unsupervised and domain independent. Its domain knowledge is provided by the comprehensive ontology of common sense knowledge contained in the Cyc knowledge base. The system described in this dissertation generates coherent and topically related new sentences as a summary for a given document. It uses syntactic structure and semantic features of the given documents to fuse information. It makes use of the knowledge base as a source of domain knowledge. Furthermore, it uses the reasoning engine to generalize novel information.
The proposed system consists of three main parts: knowledge acquisition, knowledge discovery, and knowledge representation. Knowledge acquisition derives syntactic structure of each sentence in the document and maps words and their syntactic relationships into Cyc knowledge base. Knowledge discovery abstracts novel concepts, not explicitly mentioned in the document by exploring the ontology of mapped concepts and derives main topics described in the document by clustering the concepts. Knowledge representation creates new English sentences to summarize main concepts and their relationships. The syntactic structure of the newly created sentences is extended beyond simple subject-predicate-object triplets by incorporating adjective and adverb modifiers. This structure allows the system to create sentences that are more complex. The proposed system was implemented and tested. Test results show that the system is capable of creating new sentences that include abstracted concepts not mentioned in the original document and is capable of combining information from different parts of the document text to compose a summary
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Large language models with instruction-following capabilities open the door
to a wider group of users. However, when it comes to information extraction - a
classic task in natural language processing - most task-specific systems cannot
align well with long-tail ad hoc extraction use cases for non-expert users. To
address this, we propose a novel paradigm, termed On-Demand Information
Extraction, to fulfill the personalized demands of real-world users. Our task
aims to follow the instructions to extract the desired content from the
associated text and present it in a structured tabular format. The table
headers can either be user-specified or inferred contextually by the model. To
facilitate research in this emerging area, we present a benchmark named
InstructIE, inclusive of both automatically generated training data, as well as
the human-annotated test set. Building on InstructIE, we further develop an
On-Demand Information Extractor, ODIE. Comprehensive evaluations on our
benchmark reveal that ODIE substantially outperforms the existing open-source
models of similar size. Our code and dataset are released on
https://github.com/yzjiao/On-Demand-IE.Comment: EMNLP 202
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