Release of Quanteda version 1.0

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We have announced the release of quanteda version 1.0 at the London R meeting on Tuesday. I thank all the organizers and 150+ participants. In the talk, I presented the performance comparison with R and Python packages, but I actually compared the performance with its earlier CRAN versions to show how the package evolved to be the best performing text analysis in R.

In this historical benchmaking, I measured time the earlier versions take to complete three basic operations (in second):

  • Tokenization of 6,000 newspaper articles (‘tokens’)
  • Removal of English stopwords from the tokenized texts (‘remove’)
  • Construction of document-feature matrix (‘dfm’)

The earliest versions of quanteda was fast simply because it had only limited functionality. Its tokenization and document-feature matrix construction became considerably slower from v0.8.2 as more functions, such as Unicode support, have been implemented. There was almost no change in speed until v0.9.8.5, but it token selection and document-feature matrix construction became dramatically fast in the next release. This is exactly when we introduced the upfront tokens serialization design. It only speeds up operations after tokenization, but execution time became half in tokens selection and one seventh in document-feature matrix construction!

historical benchmarking

After improving the performance, we worked hard on consistency in API and stability of C++. Besides the regression in token selection the version before 0.99.9, it has been very fast until now. Tokenization went through up and down in speed in gradual optimization to the new design, but it is also one of the fastest since v0.8.2.

A new paper on Russian media’s coverage of protests in Ukraine

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A paper ‘Russian Spring’ or ‘Spring Betrayal’? The Media as a Mirror of Putin’s Evolving Strategy in Ukraine that I co-authored with Tomila Lankina as part of the British Academy-funded project appeared in Europe-Asia Studies.

We analyse Russian state media’s framing of the Euromaidan protests using a novel Russian-language electronic content-analysis dictionary and method that we have developed ourselves. We find that around the time of Crimea’s annexation, the Kremlin-controlled media projected media narratives of protests as chaos and disorder, using legalistic jargon about the status of ethnic Russians and federalisation, only to abandon this strategy by the end of April 2014. The shift in media narratives corresponding to the outbreak of violence in the Donbas region gives credence to arguments about Putin’s strategic, interests-driven foreign policy, while adding nuance to those that highlight the role of norms and values.

I also made the longitudinal content analysis technique more accessible by updating an the LSS package.

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.

Applying LIWC dictionary to a large dataset

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LIWC is a popular text analysis package developed and maintained by Pennebaker et al. The latest version of the LIWC dictionary was released in 2015. This dictionary seems more appropriate than classic dictionaries such as the General Inquire dictionaries for analysis of contemporary materials, because our vocabulary changes over years.

However, LIWC did not work with a large corpus of news articles published between 2012-2015 (around 800MB in raw text). The error seems to show that the text file is too large for the software:

java.util.concurrent.ExecutionException: java.lang.Exception: java.lang.OutOfMemoryError: Java heap space
    at java.util.concurrent.FutureTask.report(FutureTask.java:122)
    at java.util.concurrent.FutureTask.get(FutureTask.java:192)
    at com.liwc.LIWC2015.controller.TextAnalyzer.run(TextAnalyzer.java:109)
    at com.liwc.LIWC2015.controller.MainMenuController.onAnalyzeText(MainMenuController.java:113)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at sun.reflect.misc.Trampoline.invoke(MethodUtil.java:71)
    at sun.reflect.GeneratedMethodAccessor6.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at sun.reflect.misc.MethodUtil.invoke(MethodUtil.java:275)
    at javafx.fxml.FXMLLoader$MethodHandler.invoke(FXMLLoader.java:1771)
    at javafx.fxml.FXMLLoader$ControllerMethodEventHandler.handle(FXMLLoader.java:1657)

My solution to the problem was to apply the LIWC dictionary using quanteda‘s dictionary lookup function – it could apply the dictionary to the data less the one minute on my Core i7 machine. I compared the results from quanteda and LIWC using a subset of the corpus, and found the word counts (in columns from “function” to “you” in the tables) very close to each other:

dict <- dictionary(file = './Text analysis/LIWC/LIWC2015_English_Flat.dic')
corp <- corpus(readLines('./Text analysis/Corpus/guardian_sub.txt'))
toks <- tokens(corp, remove_punct = TRUE)
toks_liwc <- tokens_lookup(toks, dict)
mx_liwc <- dfm(toks_liwc) / ntoken(toks) * 100
head(mx_liwc, 20)

Document-feature matrix of: 10,000 documents, 73 features (21.8% sparse).
(showing first 20 documents and first 6 features)
        features
docs     function   pronoun     ppron          i        we        you
  text1  43.57743  6.122449 1.4405762 0.12004802 0.7202881 0.12004802
  text2  42.94872  5.769231 0.6410256 0.00000000 0.0000000 0.00000000
  text3  43.94904  6.157113 1.6985138 0.00000000 0.2123142 0.00000000
  text4  42.12963  4.783951 1.3888889 0.15432099 0.4629630 0.15432099
  text5  40.22140  5.289053 2.7060271 0.00000000 0.6150062 0.12300123
  text6  43.44473  4.755784 0.6426735 0.00000000 0.2570694 0.00000000
  text7  41.03139  4.035874 0.2242152 0.00000000 0.0000000 0.00000000
  text8  43.82716  8.847737 6.3786008 1.02880658 0.8230453 0.00000000
  text9  42.56121  4.519774 1.3182674 0.00000000 0.3766478 0.00000000
  text10 46.11111  6.888889 1.8888889 0.44444444 0.1111111 0.22222222
  text11 49.62963 12.469136 5.5555556 1.60493827 1.1111111 0.12345679
  text12 50.00000 11.121495 6.8224299 1.02803738 2.5233645 0.00000000

Note that quanteda version 0.99 has a problem in dfm_lookup(), which slows down computation dramatically. If you want to use this function, install version 0.996 or later (available on Github).

早稲田大学で多言語テキスト分析法について発表

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早稲田大学の政治学研究科セミナーにて、『バイリンガル分析へのデータ駆動アプローチ:30年間の日英新聞における米国外交政策の表象』と題するプレゼンテーションを行いました。当プレゼンテーションは、アメリカの政治・外交について研究プロジェクトにおいて、異なる言語(英語と日本語)の文書に対して同一の量的テキスト分析手法を適用する方法に関するものです。本セミナーで発表した手法の一部は、5月22日の15時から行われる日本語の量的テキスト分析に関するワークショップでより具体的に説明します。