55,420 research outputs found
Extracting consumers needs for new products a web mining approach
Here we introduce a web mining approach for automatically identifying new product ideas extracted from web logs. A web log - also known as blog - is a web site that provides commentary, news, and further information on a subject written by individual persons. We can find a large amount of web logs for nearly each topic where consumers present their needs for new products. These new product ideas probably are valuable for producers as well as for researchers and developers. This is because they can lead to a new product development process. Finding these new product ideas is a well-known task in marketing. Therefore, with this automatic approach we support marketing activities by extracting new and useful product ideas from textual information in internet logs. This approach is implemented by a web-based application named Product Idea Web Log Miner where users from the marketing department provide descriptions of existing products. As a result, new product ideas are extracted from the web logs and presented to the users
Weak signal identification with semantic web mining
We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization's environment and that they appear in different contexts. We use internet information to represent organization's environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time
Using webcrawling of publicly available websites to assess E-commerce relationships
We investigate e-commerce success factors concerning their impact on the success of commerce transactions between businesses companies. In scientific literature, many e-commerce success factors are introduced. Most of them are focused on companies' website quality. They are evaluated concerning companies' success in the business-to- consumer (B2C) environment where consumers choose their preferred e-commerce websites based on these success factors e.g. website content quality, website interaction, and website customization. In contrast to previous work, this research focuses on the usage of existing e-commerce success factors for predicting successfulness of business-to-business (B2B) ecommerce. The introduced methodology is based on the identification of semantic textual patterns representing success factors from the websites of B2B companies. The successfulness of the identified success factors in B2B ecommerce is evaluated by regression modeling. As a result, it is shown that some B2C e-commerce success factors also enable the predicting of B2B e-commerce success while others do not. This contributes to the existing literature concerning ecommerce success factors. Further, these findings are valuable for B2B e-commerce websites creation
Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis
Notwithstanding recent work which has demonstrated the potential of using
Twitter messages for content-specific data mining and analysis, the depth of
such analysis is inherently limited by the scarcity of data imposed by the 140
character tweet limit. In this paper we describe a novel approach for targeted
knowledge exploration which uses tweet content analysis as a preliminary step.
This step is used to bootstrap more sophisticated data collection from directly
related but much richer content sources. In particular we demonstrate that
valuable information can be collected by following URLs included in tweets. We
automatically extract content from the corresponding web pages and treating
each web page as a document linked to the original tweet show how a temporal
topic model based on a hierarchical Dirichlet process can be used to track the
evolution of a complex topic structure of a Twitter community. Using
autism-related tweets we demonstrate that our method is capable of capturing a
much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, 201
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The social, political and legal aspects of Text and Data Mining (TDM)
The ideas of textual or data mining (TDM) and subsequent analysis go back hundreds if not thousands of years. Originally carried out manually, textual and data analysis has long been a tool which has enabled new insights to be drawn from text corpora. However, for the potential benefits of TDM to be unlocked, a number of non-technological barriers need to be overcome. These include legal uncertainty resulting from complicated copyright, database rights and licensing, the fact that some publishers are not currently embracing the opportunities TDM offers the academic community, and a lack of awareness of TDM among many academics, alongside a skills gap
Analysis and Detection of Information Types of Open Source Software Issue Discussions
Most modern Issue Tracking Systems (ITSs) for open source software (OSS)
projects allow users to add comments to issues. Over time, these comments
accumulate into discussion threads embedded with rich information about the
software project, which can potentially satisfy the diverse needs of OSS
stakeholders. However, discovering and retrieving relevant information from the
discussion threads is a challenging task, especially when the discussions are
lengthy and the number of issues in ITSs are vast. In this paper, we address
this challenge by identifying the information types presented in OSS issue
discussions. Through qualitative content analysis of 15 complex issue threads
across three projects hosted on GitHub, we uncovered 16 information types and
created a labeled corpus containing 4656 sentences. Our investigation of
supervised, automated classification techniques indicated that, when prior
knowledge about the issue is available, Random Forest can effectively detect
most sentence types using conversational features such as the sentence length
and its position. When classifying sentences from new issues, Logistic
Regression can yield satisfactory performance using textual features for
certain information types, while falling short on others. Our work represents a
nontrivial first step towards tools and techniques for identifying and
obtaining the rich information recorded in the ITSs to support various software
engineering activities and to satisfy the diverse needs of OSS stakeholders.Comment: 41st ACM/IEEE International Conference on Software Engineering
(ICSE2019
NLP and the Humanities: The Revival of an Old Liaison
This paper presents an overview of some\ud
emerging trends in the application of NLP\ud
in the domain of the so-called Digital Humanities\ud
and discusses the role and nature\ud
of metadata, the annotation layer that is so\ud
characteristic of documents that play a role\ud
in the scholarly practises of the humanities.\ud
It is explained how metadata are the\ud
key to the added value of techniques such\ud
as text and link mining, and an outline is\ud
given of what measures could be taken to\ud
increase the chances for a bright future for\ud
the old ties between NLP and the humanities.\ud
There is no data like metadata
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