Blog Dataset Research Articles

Social media framework for business blog mining

The explosive proliferation of blogs and new forms of social media requires new technologies to transfer the digital realm of the blogosphere into a manageable form. In this paper, we describe a social media data mining framework for business blog mining which evaluates the ability of blog-specific properties to improve classification results in our dataset of business blogs. We find that additional information contained in bloggers and links has a positive influence on our model; however, tags and time tend to have a negative influence. Our findings have implications for future work in blog mining which can leverage blog-specific properties as well as domain knowledge of blog data to improve search and retrieval performance.

A framework for building web mining applications in the world of blogs: A case study in product sentiment analysis

Recently there has been much interest in electronic commerce applications that use data mining techniques to explore datasets in the social media context. However, most of the applications have already been developed in an ad hoc manner, mainly, due to the lack of adequate tools, yielding difficulties in customizing applications and requesting high time consuming for constructing and maintaining these applications. This work addresses these problems and proposes a software framework for building Web mining applications in the blog world. The architecture of the proposed framework combines the use of blog crawling and data mining algorithms, in order to provide a complete and flexible solution for building general-purpose Web mining applications. The framework flexibility allows some important customizations, such as the construction of adapters for reading text from different blogs, and the use of different pre-processing techniques and data mining algorithms. In order to improve the efficacy of information extraction from blogs, ontology is used in the blog’s description. For this, there are software agents responsible for tracking and indexing blogs related to a specific tag and for mining blog datasets. Moreover, web services are used for encapsulating existing tools and maximize reuse. This framework has been instantiated in order to be applied for helping the blog users to effectively find out relevant information in the blog world. The focus of this paper is on describing the novel software architecture of the general framework (blog crawling and data mining) providing detailed information about the data mining sub-framework, which uses the semantic web services technology for automating service composition and consists on the main research contribution. A case study of an e-commerce application for analyzing the user’s sentiment regarding specific products is reported and its results considers the effort reduction when creating a web mining application by using the proposed integrated frameworks and existing data mining tools, as well as a qualitative analysis related to quality aspects of the developed application, such as the evolution impact.

Revised PLWAP Tree with Non-frequent Items for Mining Sequential Pattern

Sequential pattern mining is a challenging task in data mining area with large applications. One among those applications is mining patterns from weblog. Recent times, weblog is highly dynamic and some of them may become absolute over time. In addition, users may frequently change the threshold value during the data mining process until acquiring required output or mining interesting rules. Some of the recently proposed algorithms for mining weblog, build the tree with two scans and always consume large time and space. In this paper, we build Revised PLWAP with Non-frequent Items (RePLNI-tree) with single scan for all items. While mining sequential patterns, the links related to the nonfrequent items are not considered. Hence, it is not required to delete or maintain the information of nodes while revising the tree for mining updated transactions. The algorithm supports both incremental and interactive mining. It is not required to re-compute the patterns each time, while weblog is updated or minimum support changed. The performance of the proposed tree is better, even the size of incremental database is more than 50% of existing one. For evaluation purpose, we have used the benchmark weblog dataset and found that the performance of proposed tree is encouraging compared to some of the recently proposed approaches. Keywords—Sequential pattern mining; Weblog; Frequent and Non-frequent items; Incremental and Interactive mining

Exploring the Granularity Level of Social Tags

Tags are widely used in Web 2.0 applications, such as blogs and online bookmarks. Many characteristics of tags have been studied in previous works, but the granularity level of tags still remains untouched. Granularity level describes how focused a tag is, the lower the level, the more concrete concept the tag covers. The paper has three main contributions: 1) We propose a method to discover the concept level of tags automatically. 2) We apply the method to a real-world tag data set, and study the distribution of levels. 3) We perform tag recommendation concerning their concept levels. Experiments are conducted on a large-scale real world Chinese blog data set. We find that most tags are in more concrete levels, and the users tend to choose tags within the same concept level for a document. When predicting tags for a new document, tags in higher concept levels provide little help, yet may hurt the performance.

Future Link Prediction in the Blogosphere for Recommendation

The phenomenal growth in both scale and importance of social media such as blogs, micro-blogs and user-generated content, has created a need for tools that monitor information diffusion and make recommendations within these platforms. An essential element of social media, particularly blogs, is the hyperlink graph that connects various pieces of content. There are two types of links within the blogosphere; one from blog post to blog post, and another from blog post to blog channel (an event stream of blog posts). These links can be viewed as a proxy for the flow of information between blog channels and to reflect influence. Given this assumption about links, the ability to predict future links can facilitate the monitoring of information diffusion, making recommendations, and word-of-mouth (WOM) marketing. We propose different methods for link predictions and we evaluate these methods on an extensive blog dataset.

Database optimization for novelty mining of business blogs

The widespread growth of business blogs has created opportunities for companies as channels of marketing, communication, customer feedback, and mass opinion measurement. However, many blogs often contain similar information and the sheer volume of available information really challenges the ability of organizations to act quickly in today’s business environment. Thus, novelty mining can help to single out novel information out of a massive set of text documents. This paper explores the feasibility and performance of novelty mining and database optimization of business blogs, which have not been studied before. The results show that our novelty mining system can detect novelty in our dataset of business blogs with very high accuracy, and that database optimization can significantly improve the performance.

Hedonic Analysis for Estimation of Condominium Rent Utilizing WEB Information

Information in web is introduced to hedonic analysis for real estate rent. 23 wards in Tokyo are chosen for the data collection areas and rents of condominiums are estimated. To utilize the web site information, variables signifying areal reputation are generated from weblog data set. The results show that the contribution of such information varies from area to area, and that especially in a well reputed areas the contribution tend to become larger. This suggests that such information can be useful for hedonic analysis for well reputed areas, and qualitative aspects such as good townscape and sense of high class can be incorporated with this method to improve the accuracy of the rent/price estimation of real estate.

Modeling user hidden navigational behavior for Web recommendation

Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analyses of Web usage data will lead to discovering Web user access patterns, and in turn, facilitating users to locate more preferable Web contents via collaborative recommendation techniques. In the context of Web usage mining, Latent Semantic Analysis (LSA) based on probability inference provides a promising approach to capture not only user hidden navigational patterns, but also the associations between users, pages and hidden navigational patterns. The discovered user access patterns could be used as a usage reference base for identifying the new user's access preferences and making usage-based collaborative Web recommendations. In this paper, we propose a novel usage-based Web recommendation framework, in which Latent Dirichlet Allocation (LDA) is employed to learn the underlying task space from the training Web log data and infer the task distribution for a target user via task inference. The main advantages of the adapted LDA model are its capabilities of efficiently learning the semantic usage information from the Web log data and effectively inferring the access preference of the target user even with a few Web clicks that might be unseen in the training data. In this paper, we also investigate the determination of an optimizing task number, which is another important problem commonly encountered in latent semantic analysis. Experiments conducted on a real Web log dataset show that this approach can achieve better recommendation performance in comparison to other existing techniques. And the discovered task-simplex expression can also provide a better interpretation for Web designers or users to better understand the user navigational preference.

Redundancy and novelty mining in the business blogosphere

PurposeThe paper aims to explore the performance of redundancy and novelty mining in the business blogosphere, which has not been studied before.Design/methodology/approachNovelty mining techniques are implemented to single out novel information out of a massive set of text documents. This paper adopted the mixed metric approach which combines symmetric and asymmetric metrics.FindingsThe results show that the novelty mining system can detect novel and redundant blogs in the dataset of business blogs with a very high accuracy.Originality/valueThis paper shows that novelty mining techniques can be applied to business blogs to help organizations filter redundant information, and that the cosine and mixed metrics approaches produce better results.

Quantification of Information Diffusion Network Characteristics in Blogspace

This paper proposes three new quantification measures of information diffusion characteristics in blogspace. These measures are calculated from the number of three basic structures, which are directed 2-edge connected subgraphs, in information diffusion networks. Each basic structure is related to information scattering, information gathering or information transmission. We analyze and visualize information diffusion networks extracted from six blog datasets. In the result, we show that the difference of information diffusion characteristics can be discriminated by the combination of three measures and human activity in blogspace can be explained by them.

Tracking the Rise of Stem Cell Tourism

Driven by hype surrounding stem cell research, a number of clinics around the world currently offer 'stem cell therapies' to patients. These unproven interventions have attracted policy interest owing to the risks they may pose to patients and to the progress of legitimate translational stem cell research, yet remarkably little data exists about the patients who undergo these unproven therapies or their experiences. We sought to characterize this patient population. We developed a comprehensive data set of blogs written by patients (or their caretakers) about their experiences with unproven stem cell therapies. Analyzing these data suggests that unproven stem cell therapies are increasing rapidly in popularity and are attracting a wide range of patients--both young and old and with a diverse collection of medical conditions. These results should help clinicians advise individual patients and help policymakers devise strategies to mitigate the risks these treatments pose.

Incremental spectral clustering by efficiently updating the eigen-system

In recent years, the spectral clustering method has gained attentions because of its superior performance. To the best of our knowledge, the existing spectral clustering algorithms cannot incrementally update the clustering results given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as websphere or blogsphere. Unlike the traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing points. In this paper, we extend the standard spectral clustering to such evolving data, by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigen-system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by the standard spectral clustering, it achieves similar accuracy but with much lower computational cost. It can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs. The core technique of incrementally updating the eigenvalue system is a general algorithm and has a wide range of applications—as well as incremental spectral clustering—where dynamic graphs are involved. This demonstrates the wide applicability of our incremental algorithm.

Information discovery across multiple streams

In this paper we address the issue of continuous keyword queries on multiple textual streams and explore techniques for extracting useful information from them. The paper represents, to our best knowledge, the first approach that performs keyword search on a multiplicity of textual streams. The scenario that we consider is quite intuitive; let’s assume that a research or financial analyst is searching for information on a topic, continuously polling data from multiple (and possibly heterogeneous) text streams, such as RSS feeds, blogs, etc. The topic of interest can be described with the aid of several keywords. Current filtering approaches would just identify single text streams containing some of the keywords. However, it would be more flexible and powerful to search across multiple streams, which may collectively answer the analyst’s question. We present such model that takes in consideration the continuous flow of text in streams and uses efficient pipelined algorithms such that results are output as soon as they are available. The proposed model is evaluated analytically and experimentally, where the Enron dataset and a variety of blog datasets are used for our experiments.

Content Based Recommendation and Summarization in the Blogosphere

This paper presents a stochastic graph based method for recommending or selecting a small subset of blogs that best represents a much larger set. within a certain topic. Each blog is assigned a score that reflects how representative it is. Blog scores are calculated recursively in terms of the scores of their neighbors in a lexical similarity graph. A random walk is performed on a graph where nodes represent blogs and edges link lexically similar blogs. Lexical similarity is measured using either the cosine similarity measure, or the Kullback-Leibler (KL) divergence. In addition, the presented method combines lexical centrality with information novelty to reduce redundancy in ranked blogs. Blogs similar to highly ranked blogs are discounted to make sure that diversity is maintained in the final rank. The presented method also allows us to include additional initial quality priors to assess the quality of the blogs, such as frequency of new posts per day and the text fluency measured by n-gram model probabilities, etc. We evaluate our approach using data from two large blog datasets. We measure the selection quality by the number of blogs covered in the network as calculated by an information diffusion model. We compare our method to other heuristic and greedy selection methods and show that it significantly outperforms them.

Exploring website log data with coordinated dimension hierarchies and network pivots

Many interfaces exist for exploring multi-dimensional data, but most are not suitable for OLAP as they do not support aggregation and selection via dimension hierarchies. We introduce an interface for exploring OLAP data via coordinated dimension hierarchies and network views. Three dimension coordinations are identified; progressive, global and result only, for filtering and presenting data. We integrate these with a network pivot that supports joins through an arbitrary relation. We demonstrate with an example web log dataset of site visits organised into time, downloads, visitor address and referrer address dimensions and a network of prior and later visits.

Filter co-ordinations for exploring multi-dimensional data

Many interface designs have been developed for the exploration of multi-dimensional data sets which are based on finding subsets by filtering attribute values. Systems such as dynamic queries use a collection of independent filters to interactively query by restricting attribute values. However, for large data sets there is a need for an alternative style of filtering that better supports stepwise query refinement. This article introduces a new filter coordination which supports both stepwise query refinement and independent filters. Our filter visualization also supports the visualization of attribute value hierarchies enabling multi-level data distribution overviews to be given. Our coordination design is implemented in our SGViewer query tool which we demonstrate with a multi-dimensional web log data set. An evaluation of SGViewer showed that after a short learning period users were able to use it to read trends and proportions and make drill-down queries.