As technology has continually advanced throughout the years, digital humanities tools, such as literary and text analysis, have likewise modernized through the development of various machine learning methods. While tools have evolved significantly in this field, it is necessary to confront the ways in which many of these digital tools stem from and maintain colonialism. Countless have taken note of this issue and collectively work towards decolonizing the humanities: an ongoing initiative that strives to create new tools that centralize minoritized voices and experiences while simultaneously countering traditional colonialist technologies that promote a humanities dominated by whiteness and androcentrism. 

A method within the digital humanities that is exemplary of this kind of work is feminist text analysis: this paper not only insists upon the existence of feminist text analysis but also explores the crucial role that it plays in challenging androcentric narratives and hierarchies of knowledge that arise from legacies of colonialism. Through analyzing Sara Mill’s “Post-Feminist Text Analysis” article in which the English linguist implements a feminist text analysis that considers how overt sexism of the past has mutated into a more indirect, inconspicuous sexism shrouded by a false veneer of gender inclusivity, the capabilities of feminist text analysis are showcased.

I argue that based on this example along with myriad others ranging from analysis of book reviews in The New York Times to analysis of dialogue in Disney films, there is such a thing as a feminist text analysis and that it plays an important role in decolonizing the digital humanities.  

References:

Mills, Sara. “Post-Feminist Text Analysis.” Language and Literature, vol. 7, no. 3, Aug. 1998, pp. 235–252

For today’s class I chose the article Gender Dynamics and Critical Reception: A Study of Early 20th-century Book Reviews from The New York Times by Matthew J. Lavin. In this article, Lavin details his analysis of 2800 book reviews published in The New York Times from the early 20^th-century, employing multiple machine learning scenarios to explore how these book reviews furthered sexist outlooks on female authorship and “gendered norms for reading and readership.”  

After collecting his data online from The New York Times, Lavin utilized a logistic regression model, where data is divided into train and test sets and then compared. In this experiment, the book reviews acted as the training set and a series of predictions acted as the test set. Using this model, Lavin investigated how well a logistic regression model could predict the gender of an author. Lavin employed several scenarios using the logistic regression model: first no terms were removed, then stop words were removed, then stop words, gender nouns, and titles removed, and finally stop words, gender nouns, titles, and common forenames removed. After each scenario, Lavin examined the top coefficients for what the model predicted as male and female writers. Scenario 1’s results generated an accuracy rate of 78%-90%. However, as gendered terms and other words are removed, accuracy begins to decrease. Through lemmatization, Lavin consolidates frequently used terms and categories based on the female and male coefficients found in the 4^th and final scenario. Lavin uses the example lemma ‘child.’ This term is found in 700 reviews, notably 36% female labeled and only 24% male labeled. 

Based on the results of the four scenarios, Lavin notes that most female labeled lemma coefficients tend to be related to domestic settings, marriage, and aesthetics, while male labeled lemma coefficients tend to be related to prestige, power, military and government. Moreover, Lavin notes that non-fiction is more associated with male writers, while fiction is more associated with female writers. This is notable as it relates to the topic of middlebrow literature and how often this type of literature is associated with ‘cheap fiction,’ sentimental themes, mediocrity, and “aimless, indolent and ardent femininity.” Lavin’s final thoughts asserted that the results of his analysis showed evidence that, “behind a veneer of neutrality, gender norms were being established, parameterized, and lined to taste-making.”  

Final Thoughts:  

The impact that reviews have in our modern culture is undeniable. Whether one is looking for a book to read or trying out a new restaurant, most know that reviews that be found online quickly and seamlessly. Negative reviews can make or break a business, and for things like movies and books, the opinions of self-proclaimed expert critics can have an immense impact on overall reception of the work. I found this article so interesting because I failed to consider the genesis of these kinds of reviews and how they could so negatively impact perception of certain types of writing, themes and authorship. I think this is a theme that could be further explored in modern reviews of books, television shows or movies; the results of modern reviews could be then compared to older reviews and see if gendered norms have changed or not over the years.  

This article explores the impact early 20th-century book reviews in The New York Times had on gendering authors, reception landscapes of works associated with male and female authors, and the feminization of what was considered middlebrow literature.