R’s tidytext turns messy text into valuable insight “Many of us who work in analytical fields are not trained in even simple interpretation of natural language,” write Julia Silge, Ph.D., and David Robinson, Ph.D., in their newly released book Text Mining with R: A tidy approach. The applications of text mining are numerous and varied, though; sentiment analysis can assess the emotional content of text, frequency measurements can identify a document’s most important terms, analysis can explore relationships and connections between words, and topic modeling can classify and cluster similar documents. I recently caught up with Silge and Robinson to discuss how they’re using text mining on job postings at Stack Overflow, some of the challenges and best practices they’ve experienced when mining text, and how their tidytext package for R aims to make text analysis both easy and informative. Let’s start with the basics. Why did you create the tidytext text mining package in R? We are swimming in text data at Stack Overflow!
Campaigns by You Hollywood Stock Exchange Is Becoming A Real Money Exchange In April. Seriously. I’m a bit of a movie fanatic. As such, back in the day one of my favorite websites was Hollywood Stock Exchange (HSX). On it, you bought and sold both movies (moviestocks) and movie stars (starbonds) based on how you thought they would do with upcoming releases. On April 20, HSX will become a real-money commodity exchange, according to The Hollywood Reporter. As THR notes: Once launched, a new HSX site will list current and imminent movie releases with their projected four-week domestic grosses and allow exchange users to take long or short positions on the films. And: Investors wishing to participate in the exchange will buy “contracts” priced at one one-millionth of a film’s projected boxoffice, with films to be listed on the exchange from the time productions are announced in the industry trade papers. Cantor Exchange, which is working with HSX on this transition to real money has more details and is kicking things off with a practice exchange until the real one is approved:
R Programming/Text Processing This page includes all the material you need to deal with strings in R. The section on regular expressions may be useful to understand the rest of the page, even if it is not necessary if you only need to perform some simple tasks. This page may be useful to : perform statistical text analysis.collect data from an unformatted text file.deal with character variables. In this page, we learn how to read a text file and how to use R functions for characters. help.search(keyword = "character", package = "base") However, their name and their syntax is not intuitive to all users. Keywords : text mining, natural language processingSee CRAN Task view on Natural Language Processing[2]See also the following packages tm, tau, languageR, scrapeR. Reading and writing text files[edit] R can read any text file using readLines() or scan(). We can write the content of an R object into a text file using cat() or writeLines(). Character encoding[edit] Example[edit] The following example was run under Windows. >?
PSPP Features[edit] This software provides a basic set of capabilities: frequencies, cross-tabs comparison of means (t-tests and one-way ANOVA); linear regression, logistic regression, reliability (Cronbach's Alpha, not failure or Weibull), and re-ordering data, non-parametric tests, factor analysis, cluster analysis, principal components analysis, chi-square analysis and more. At the user's choice, statistical output and graphics are available in ASCII, PDF, PostScript, SVG or HTML formats. A range of statistical graphs can be produced, such as histograms, pie-charts scree plots and np-charts. PSPP can import Gnumeric and OpenDocument spreadsheets, Postgres databases, comma-separated values and ASCII files. It can export files in the SPSS 'portable' and 'system' file formats and to ASCII files. Origins[edit] Release history[edit] Third Party Reviews[edit] See also[edit] References[edit] External links[edit] Official website Third-party resources[edit]
Twitter predicts the future? A recent study [pdf] by Sitaram Asur and Bernardo A. Huberman at HP Labs found that it's possible to use Twitter chatter to predict first-weekend box office revenues simply based on volume of tweets. The predictions were even more accurate when they introduced sentiment analysis (i.e. classified tweets as positive or negative). The above chart shows predicted revenue on the first weekend versus actual. What you see are predictions that are more or less within in couple hundred thousand of the actual openings. Asur and Huberman note that their tweet-based model outperformed HSX, which is interesting because HSX is switching to real money soon. This also has implications in predicting other stuff that involves the opinion of the masses, for example, who is going to win an election or how well a product will sell.
Text Mining with R In text mining, we often have collections of documents, such as blog posts or news articles, that we’d like to divide into natural groups so that we can understand them separately. Topic modeling is a method for unsupervised classification of such documents, similar to clustering on numeric data, which finds natural groups of items even when we’re not sure what we’re looking for. Latent Dirichlet allocation (LDA) is a particularly popular method for fitting a topic model. As Figure 6.1 shows, we can use tidy text principles to approach topic modeling with the same set of tidy tools we’ve used throughout this book. Latent Dirichlet allocation Latent Dirichlet allocation is one of the most common algorithms for topic modeling. Every document is a mixture of topics. LDA is a mathematical method for estimating both of these at the same time: finding the mixture of words that is associated with each topic, while also determining the mixture of topics that describes each document.
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A Statistical Analysis of the Work of Bob Ross Bob Ross was a consummate teacher. He guided fans along as he painted “happy trees,” “almighty mountains” and “fluffy clouds” over the course of his 11-year television career on his PBS show, “The Joy of Painting.” In total, Ross painted 381 works on the show, relying on a distinct set of elements, scenes and themes, and thereby providing thousands of data points. I decided to use that data to teach something myself: the important statistical concepts of conditional probability and clustering, as well as a lesson on the limitations of data. So let’s perm out our hair and get ready to create some happy spreadsheets! More Culture What I found — through data analysis and an interview with one of Ross’s closest collaborators — was a body of work that was defined by consistency and a fundamentally personal ideal. I analyzed the data to find out exactly what Ross, who died in 1995, painted for more than a decade on TV. Conditional probability can be a bit tricky. What about footy little hills?
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