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Text of the page (random words):
rful job compiling relevant demographic information but in so doing they rightly recognize that interpreting the information both historically and in the present moment is another job altogether the data are separated from their explanation spahr and young are i imagine on the political left but their data remain open to explanation from multiple political or apolitical perspectives from an apolitical perspective i would want to explain some of their demographic data with simple demography for example they imply that 29 non white representation in english phd programs is not enough but america is precisely 29 non white and 71 white so i don t find that statistic problematic at all i would also claim that this same demographic point partially ameliorates the 18 non white representation in mfa programs though obviously a gap in representation remains how to explain it though their essay is rightly not ideological enough to foreclose on all but a single left facing window of possibility this is a good thing recognizing the possibility of multiple explanations is what keeps a field of inquiry from becoming an ideological echo chamber spahr et al also point to sociology as a field that uses computational methods to address critical cultural questions but again addressing critical or cultural questions with computational methods is not at all the same thing as being critical culturally progressive or activist sociologists and psychologists have i think always recognized if only quietly that progressive or activist readings of their data are by no means the only readings steven pinker and jon haidt among others are really pushing the point lately with their heterodox academy it s all a big debate of course but that s the point in my view good computational scholarship opens up debate and rarely points to one single and obvious and you re stupid if you don t believe it conclusion sometimes it does but that s usually in the context of not immediately political content e g whether or not piraha possesses recursive syntax but when you re talking about large social or political explanations i ve never seen the explanation that doesn t leave me thinking mm maybe interesting i dunno we ll see i m sure my skepticism comes across as conservatism to some from my perspective as a scholar however i m simply tentative about my own worldview i m therefore deeply suspicious of any scholar or study purporting to provide 100 support for any particular ideology or political platform so i think it s a good thing that a lot of dh work doesn t do that indeed i m drawn most often to theories that piss off everyone across the political spectrum e g gregory clark s work because my most deeply held prior is that the world as it is probably won t conform very often to any particular ideology or politics if anything then i d like to see more dh work not confirming a single orthodoxy but challenging many orthodoxies all at once then i ll be confident it s doing something right posted in uncategorized leave a comment march 30 2016 by seth long readability formulas readability scores were originally developed to assist primary and secondary educators in choosing texts appropriate for particular ages and grade levels they were then picked up by industry and the military as tools to ensure that technical documentation written in house was not overly difficult and could be understood by the general public or by soldiers without formal schooling there are many readability metrics nearly all of them calculate some combination of characters syllables words and sentences most perform the calculation on an entire text or a section of a text a few like the lexile formula compare individual texts to scores from a larger corpus of texts to predict a readability level the most popular readability formulas are the flesch and flesch kincaid flesch readability formula flesch kincaid grade level formula the flesch readability formula last chapter in the link results in a score corresponding to reading ease difficulty counterintuitively higher scores correspond to easier texts and lower scores to harder texts the highest easiest possible score tops out around 120 but there is no lower bound to the score wikipedia provides examples of sentences that would result in scores of 100 and 500 the flesch kincaid grade level formula was produced for the navy and results in a grade level score which can be interpreted also as the number of years of education it would take to understand a text easily the score has a lower bound in negative territory and no upper bound though scores in the 13 20 range can be taken to indicate a college or graduate level grade so why am i talking about readability scores one way to understand distant reading within the digital humanities is to say that it is all about adopting mathematical or statistical operations found in the social natural physical or technical sciences and adapting them to the study of culturally relevant texts e g matthew jocker s use of the fourier transform to control for text length ted underwood s use of cosine similarity to compare topic models even topic models themselves which come out of information retrieval as do many of the methods used by distant readers these examples could be multiplied thus i m always on the lookout for new formulas and python codes that might be useful for studying literature and rhetoric readability scores it turns out have sometimes been used to study presidential rhetoric specifically they have been used as proxies for the intellectual quality of a president s speech writing most notably elvin t lim s the anti intellectual presidency applies the flesch and flesch kincaid formulas to inaugurals and states of the union discovering a marked decrease in the difficulty of these speeches from the 18th to the 21st centuries he argues that this decrease should be understood as part and parcel of a decreasing intellectualism in the white house more broadly ten seconds of googling turned up a nice little python library textstat that offers 6 readability formulas including flesch and flesch kincaid i applied these two formulas to the 8 spoken written sotu pairs i ve discussed in previous posts i also applied them to all spoken vs all written states of the union copied chronologically into two master files here are the results s spoken w written flesch readability scores for states of the union lower score more difficult flesch kincaid grade level scores for states of the union the obvious trend uncovered is that written states of the union are a bit more difficult to read than spoken ones contra rule et al 2015 this supports the thesis that medium matters when it comes to presidential address presidents simplify or as lim might say they dumb down their style when addressing the public directly they write in a more elevated style when delivering written messages directly to congress for the study of rhetoric then readability scores can be useful proxies for textual complexity it s certainly a useful proxy for my current project studying presidential rhetoric i imagine they could be useful to the study of literature as well particularly to the study of the literary public and literary economics does reading difficulty correspond with sales with popular vs unknown authors with canonical vs non canonical texts which genres are more difficult and which ones easier of course like all mathematical formula applied to culture readability scores have obvious limitations for one they were originally designed to gauge the readability of texts at the primary and secondary levels even when adapted by the military and industry they were meant to ensure that a text could be understood by people without college educations or even high school diplomas thus as begeny et al 2013 have pointed out these formulas tend to break down when applied to complex texts flesch kincaid grade level scores of 6 vs 10 may be meaningful but scores of say 19 vs 25 would not be so straightforward to interpret also like most nlp algorithms the formulas take as inputs things like characters syllables and sentences and are thus very sensitive to the vagaries of natural language and the influence of individual style steinbeck and hemingway aren t easy reads but because both authors tend to write in short sentences and monosyllabic dialogue their texts are often given scores indicating that 6th grades could read them no problem and authors who use a lot of semi colons in place of periods may return a more difficult readability score than they deserve since all of these algorithms equate long sentences with difficult reading however i imagine this issue could be easily dealt with by marking semi colons as sentence dividers all proxies have problems but that s never a reason not to use them i d be curious to know if literary scholars have already used readability scores in their studies they re relatively new to me though so i look forward to finding new uses for them posted in uncategorized 1 comment march 28 2016 by seth long cosine similarity parameters tf idf or boolean in a previous post i used cosine similarity a vector space model to compare spoken vs written states of the union in this post i want to see whether and to what extent different metrics entered into the vectors either a boolean entry or a tf idf score change the results first here s a brief recap of cosine similarity one way to quantify the similarity between texts is to turn them into term document matrices with each row representing one of the texts and each column representing every word that appears in both of the texts the matrices will be sparse because each text contains only some of the words across both texts with these matrices in hand it is a straightforward mathematical operation to treat them as vectors in euclidean space and calculate their cosine similarity with the euclidean dot product formula which returns a metric between 0 and 1 where 0 no words shared and 1 exact copies of the same text but what exactly goes into the vectors in these matrices not words from the two texts under comparison obviously but numeric representations of the words the problem is that there are different ways to represent words as numbers and it s never clear which is the best way when it comes to vector space modeling i have seen two common methods the boolean method if a word appears in a text it is represented simply as a 1 in the vector if a word does not appear in a text it is represented as a 0 the tf idf method if a word appears in a text its term frequency inverse document frequency is calculated and that frequency score appears in the vector if a word does not appear in a text it is represented as a 0 in my previous post i used this python script compliments to dennis muhlstein which uses the boolean method tf idf scores control for document length which is important sometimes but i wasn t sure if i wanted to ignore length when analyzing the states of the union after all if a change in medium induces a change in a speech s length that s a modification i d like my metrics to take note of but how different would the results be if i had used tf idf scores in the term document matrices that is if i had controlled for document length when comparing written vs spoken states of the union using scikit learn s tfidfvectorizer and its cosine similarity function part of the pairwise metrics module i again calculated the cosine similarity of the written and spoken addresses but this time using tf idf scores in the vectors the results of both methods boolean and tf idf are graphed below i graphed the blue tf idf measurements first in decreasing order beginning with the most similar pair nixon s 1973 written spoken addresses and ending with the most dissimilar pair eisenhower s 1956 addresses then i graphed the boolean measurements following the same order i ended each line with a comparison of all spoken and all written states of the union 1790 2015 copied chronologically into two master files in general both methods capture the same general trend though with slightly different numbers attached to the trend in a few cases these discrepancies seem major with tf idf scores nixon s 1973 addresses returned a cosine similarity metric of 0 83 with boolean entries the same addresses returned a cosine similarity metric of 0 62 and when comparing all written spoken addresses the tf idf method returned a similarity metric of 0 75 the boolean method returned a metric of only 0 55 so even though both methods capture the same general trend tf idf scores produce results suggesting that the spoken written pairs are more similar to each other than do the boolean entries these divergent results might warrant slightly different analyses and conclusions not wildly different of course but different enough to matter so which results most accurately reflect the textual reality well that depends on what kind of textual reality we re trying to model controlling for length obviously makes the texts appear more similar so the right question to ask is whether or not we think length is a disposable feature a feature producing more noise than signal i m inclined to think length is important when comparing written vs spoken states of the union so i d be inclined to use the boolean results either way my habit at the moment is to make parameter adjustment part of the fun of data analysis rather than relying on the default parameters or on whatever parameters all the really smart people tend to use the smart people aren t always pursuing the same questions that i m pursuing as a humanist who studies rhetoric another issue raised by this method comparison is the nature of the cosine similarity metric itself 0 no words shared 1 exact copies of the same text but that leaves a hell of a lot of middle ground what can i say ultimately and from a humanist perspective about the fact that nixon s 1973 addresses have a cosine similarity of 0 83 while eisenhower s 1956 addresses have a cosine similarity of 0 48 a few days ago i found and subsequently lost and now cannot re find a quora thread discussing common sense methods for interpreting cosine similarity scores and all the answers recommended using benchmarks finding texts from the same or a similar genre as the texts under comparison that are commonly accepted to be exceedingly different or exceedingly similar asking a small group of readers to come up with these judgments can be a good idea here so for example if using this method on 19th century english novels a good place to start would be to measure say moby dick and pride and prejudice two novels that a priori we can be absolutely sure represent wildly different specimens from a semantic and stylistic standpoint and indeed the cosine similarity of melville s and austen s novels is only 0 24 there s a dissimilarity benchmark set at the similarity end we might compute the cosine similarity of say pride and prejudice an...