Beautiful soup
Isn't Beautiful soup primarily to do screen scraping? I already scraped the content and stored it in a DB. Now I want to do some analysis on the text.
Also Beautiful Soup is no longer supported. Use html5lib.
What tools do you recommend for text mining?
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Re: What tools do you recommend for text mining?
#12Gensim has LSI and SVD clustering for data, and is in Python. http://nlp.fi.muni.cz/projekty/gensim/
There is also SVM Lite, which can do much of the same things with potentially less work from you. I've not used it, so I don't know how well it works. http://svmlight.joachims.org/
Re: What tools do you recommend for text mining?
#13You have not specified clearly what result you want.
If you want to apply existing tools to solve particular known problems, you might want to look at Tony Segaran's Programming Collective Intelligence (2007) for a survey of the sort of things people have done. Or ask his list for what kinds of things you want to learn from this data.
If you are wanting to discover new relationships between the various, there are tools for that as well. See. for example, http://people.ischool.berkeley.edu/~hearst/papers/acl99/acl9.... Systems that create knowledge from data by some independent process are still rare and sketchy.
Re: What tools do you recommend for text mining?
#14A useful survey of algorithms--
Top 10 algorithms in data mining Knowledge and Information Systems archive Volume 14 , Issue 1 (December 2007) table of contents Pages: 1-37 Year of Publication: 2007 ISSN:0219-1377
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.