Historical analysis of NYT using web API

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We usually use commercial database such as Nexis to download news stories in the past, but you should use New York Times APIs if you want to do historical analysis of news content. We can search NYT news articles until 1851 through the API, and it is free for anyone! We can only download meta-data, including summary texts (lead paragraphs), but we can still do a lot of content analysis with it.

You have to collect a lot of items when each text is short. It should not be difficult to so through the API if you use rtimes package. However, it is actually not as easy as it sound, because web APIs sometimes do not respond, and we can only call the API 1000 times a day. Therefore, our downloader have to be robust against unstable connections, and able to resume downloading next day.

After several attempts, I managed to run download without unexpected errors. Using the code below, you can download summaries of NYT articles that contain ‘diplomacy’ or ‘military’ in their main texts between 1851 and 2000. This program saves downloaded data yearly to RSD files, so that you do not loose, even if you have to restart your R. Do not forget to replace xxxxxxxxxxxxxxxxxxxxxxxxxxxx wit your own API key.


#install.packages("rtimes") rm(list=ls()) require(rtimes) require(plyr) httr::config(timeout = 120) query <- '(body:"diplomacy" OR body:"military")' field <- c("_id", "page", "snippet", "word_count", "score", "headline.main", "headline.print_headline", "byline.original", "web_url") fetch <- function(query, year, page) { res <- as_search(q = NULL, fq = query, begin_date = paste0(year, "0101"), end_date = paste0(year, '1231'), key = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxx', page = page, fl = c('_id', 'pub_date', 'word_count', 'snippet', 'headline', 'section_name', 'byline', 'web_url')) return(res) } for (year in seq(1851, 2000)) { if (file.exists(paste0('API/temp/', year, '.RDS'))) { cat('Skip', year, "\n") next } cat('Seach', year, "\n") data <- data.frame() res <- NULL page <- 1 while (is.null(res) || res$meta$hits > 10 * page) { res <- NULL attempt <- 0 while (is.null(res) && attempt <= 5) { attempt <- attempt + 1 try( res <- fetch(query, year, page) ) if (is.null(res)) { cat('Error', attempt,'\n') Sys.sleep(30) } if (attempt > 5) { stop('Aborted\n') } } if (nrow(res$data) == 0) { cat('No data\n') break } res$data$page <- page data <- rbind.fill (data, res$data) cat(10 * page, 'of', res$meta$hits, "\n") Sys.sleep(5) page <- page + 1 } if (nrow(data) > 0) { data$year <- year saveRDS(data, file = paste0('API/temp/', year, '.RDS')) } Sys.sleep(5) }

What is the best SVD engine for LSA in R?

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I use latent semantic analysis (LSA) to extract synonyms from a large corpus of news articles. I was very happy with Gensim‘s LSA function, but I was not sure how to do LSA in R as good as in Python. There is an R package called lsa, but it is unsuitable for large matrices, because its underlying function svd() calculates all the singular values. Since I usually split documents into sentences in this task, my document-feature matrix is very large and extremely sparse.

It is easy to make an LSA function myself, but the question is which is the best SVD engine in R for this application? rsvd, irlba or RSpectra? The authors claim that their package is the fastest, but it seems depending on the size of the matrix to decompose and the number of singular values to ask for. rsvd seems very fast with small matrices, but it used more than 20GB of RAM on my Linux machine for a matrix created from only 1,000 news articles, while irlba and RSpectra require much less memory space.

I compared irlba and RSpectra in terms of its speed and accuracy using corpora in different sizes. The original corpus is comprised on 300K full-text New York Times news stories on politics. I randomly sampled news stories to construct sub-corpus and removed function words using quanteda for this benchmarking. Arguments of the functions are set in the following way:

# irlba
S <- irlba::irlba(x, nv = 300, center = Matrix::colMeans(x), verbose = FALSE, right_only = TRUE, tol = 1e-5)

# RSpectra
S <- RSpectra::svds(x, k = 300, nu = 0, nv = 300, opts = list(tol = 1e-5))

It is straight forward to measure the speed of the SVD engines: repeatedly create sub-corpora of between 1-10K documents, and record execution time. The result shows that RSpectra is roughly 5 times faster than irlba regardless of the sizes of the corpora.

It is more difficult to gauge the quality of SVD, but I achieved this by calculating cosine similarity of words to an English verb and counting its word stems in top 100 words. For example, when most similar words to ‘ask’ are extracted based on cosine similarity, I expected to find its inflicted forms such as ‘asked’, ‘asks’, ‘asking’ in the top 100 if decomposition is accurate. I cannot tell how many inflicted forms they should extract, but a larger number for the same word suggests higher accuracy. I used 25 common English words, and calculated average number of such words here.

word <- c('want', 'use', 'work', 'call', 'try', 'ask', 'need', 'seem', 
          'help', 'play', 'move', 'live', 'believe', 'happen', 'include', 
          'continue', 'change', 'watch', 'follow', 'stop', 'create', 'open', 
          'walk', 'offer', 'remember')

The differences between RSpectra and irlba aren’t large, but the former still outperformed the latter in all the croups sizes. It is surprising that RSpectra did not compromise its accuracy for its speed. Interestingly, the the curves for both package become flat on the right-hand side, suggesting there is no need to construct corpus larger than 8K documents (~400K sentences) for synonym extraction tasks.

My conclusion based on this benchmarking is that RSpectra is the best for LSA application in R. Nonetheless, since irlba is being actively developed to improve its performance, we should keep eyes of the package too.