Pbm: A new dataset for blog mining by Mehwish Aziz and Muhammad Rafi.
Abstract:
Text mining is becoming vital as Web 2.0 offers collaborative content creation and sharing. Now Researchers have growing interest in text mining methods for discovering knowledge. Text mining researchers come from variety of areas like: Natural Language Processing, Computational Linguistic, Machine Learning, and Statistics. A typical text mining application involves preprocessing of text, stemming and lemmatization, tagging and annotation, deriving knowledge patterns, evaluating and interpreting the results. There are numerous approaches for performing text mining tasks, like: clustering, categorization, sentimental analysis, and summarization. There is a growing need to standardize the evaluation of these tasks. One major component of establishing standardization is to provide standard datasets for these tasks. Although there are various standard datasets available for traditional text mining tasks, but there are very few and expensive datasets for blog-mining task. Blogs, a new genre in web 2.0 is a digital diary of web user, which has chronological entries and contains a lot of useful knowledge, thus offers a lot of challenges and opportunities for text mining. In this paper, we report a new indigenous dataset for Pakistani Political Blogosphere. The paper describes the process of data collection, organization, and standardization. We have used this dataset for carrying out various text mining tasks for blogosphere, like: blog-search, political sentiments analysis and tracking, identification of influential blogger, and clustering of the blog-posts. We wish to offer this dataset free for others who aspire to pursue further in this domain.
This paper details construction of the blog data set used in Sentence based semantic similarity measure for blog-posts.
The aspect I found most interesting was the restriction of the data set to a particular domain. When I was using physical research tools (books) in libraries, there was no “index to everything” available. Nor would I have used it had it been available.
If I had a social science question (political science major) or later a law question (law school), I would pick a physical research tool (PRT) that was appropriate to the search request. Why? Because specialized publications were curated to facilitate research in a particular area, including identification of synonyms and cross-referencing of information you might otherwise not notice.
Is this blogging dataset a clue that if we created sub-sets of the entire WWW, that we could create indexing/analysis routines specific to those datasets? And hence give users a measurably better search experience?