The Venetian Project: The Power of Quantitative Analysis for Literary Interpretation

By Lillian Lim, member of The Venetian Project Team

The Venetian Project:

The Power of Quantitative Analysis for Literary Interpretation in Shakespeare’s The Merchant of Venice

     As years of close-reading strongly built and compiled most of the world’s criticisms of literature, literary analysts, teachers, students, and the common reader alike, continue to seek fresh and interesting means to understand text. As a result of ongoing technological advancements, the creation of digital mapping tools, text-analysis programs, and visualization tools allow for people to obtain this higher, more in-depth analytical experience. In the attempt to offer a basic idea of how pattern-discovery and visualized data entries can contribute to richer literary interpretations, The Venetian Project Team aims to encourage readers to step away from close-reading by constructing a website to illustrate the approach.

     Although close-reading is a standard approach and an effective means to interpret text, The Venetian Project exemplifies the advantages of interpreting literature by use of data-driven text analysis tools. By running Shakespeare’s entire Merchant of Venice dialogue through Tagcrowd, WordHoard and PieSpy, The Venetian Project team studied characters by looking at generated word frequencies and relationship mappings.

     The Venetian Project Team chose to study Shakespeare’s Merchant of Venice based on how it successfully met the requirements of this criterion: rich themes, provoking dialogue, online text accessibility, and the potential for interesting (tool generated) visual appeal. The Merchant of Venice is a play that takes place in Venice and Belmont. When Portia’s father dies in Belmont, she is left with a fortune in addition to instructions stipulating that her suitors must pass an unconventional test in order to win her hand in marriage. Her first two suitors chose from three chests; gold, silver, and lead; but failed to choose the correct one. At this time, a young man named Bassannio endeavors to obtain the monetary means to travel to Belmont, while his dear friend Antonio aids him in this task. Antonio borrows ducats from a Jewish moneylender named Shylock and signs a contract with him that binds Antonio to provide a pound of his own flesh as collateral for the loan. When Bassannio finally passes the test and wins Portia’s hand in marriage, the bulk of the plot commences. Following the happy moment, one of Antonio’s ships has gone missing and seeing as how repayment is impossible, Shylock immediately demands a pound of Antonio’s flesh. Shylock is unforgiving and resentful because of how Antonio and other Christians have berated him over the years for usury, and the persecution he receives becomes a propelling force for his actions. At this time, Shylock’s servant Lancelot abandons him to work for Bassannio, and his daughter Jessica has run away to marry another Christian named Lorenzo, who is also friends with Antonio and Bassannio. This leaves Shylock furious and vengeful; therefore when Portia hears of the collateral and provides Basssanio the money for repayment, Shylock rejects it and settles their situation in a trial held in Venice. The Duke acts as presiding judge, and while he dislikes Shylock, he understands the importance of adhering to contracts. Portia arrives in court disguised as a male lawyer and accuses Shylock of threatening the life of a Venetian citizen. As a result of the trial, Antonio is pardoned and Shylock is forced to relinquish his property and his Jewish faith. The play follows with a focus on the relationships between Portia and Bassannio, Graziano and Nerissa, and Lorenzo and Jessica, and concludes with arriving news that Antonio’s ships in fact did not sink and returned in one peace. Everyone celebrates their newfound lives, while Shylock is stripped away of all fortune and belongings, and left further alienated and forgotten.

     A close-reading of Shakespeare’s Merchant of Venice provides that it addresses themes of self-interest, love, cross-dressing, filial piety, religion and law. With this in mind, The Venetian Project team started off by asking, “What else could we discover or notice about this text by analyzing it through Tagcrowd, Wordhoard, and PisSpy”?

     Team members began by constructing a list of all characters within the play. After ranking each character by order of total dialogue length, team members ran character dialogues through Tagcrowd. Tagcrowd is a program that counts the amount of times all words within a body of text is present, and displays each word where its size (progressively big or small) depends on how often or how rare it is mentioned. The most useful tool it contains is a word frequency count, and the results were used to construct “Individual Character Analysis” graphs. Under this section, each character has a graph to display their top three word frequencies and a short criticism alongside it to explain those findings.

     The team approached every character evaluation with the notion that words with high frequency reveal what are most important to each character, and as a result, came upon interesting findings. Knowing that Antonio and Bassanio are best friends, Tagcrowd results brought forth inquiries that question where to draw the line between friendship and homosexuality. The most frequent word that Antonio states is Bassannio’s name, and the topic of this in James O’Rourke’s article titled “Racism and Homophobia in the Merchant of Venice” only gains further complement from the team’s word frequency results. Another character that provided interesting results was the character of Shylock. He mentions the terms: “bonds”, “thou”, “ducats”, and “Christian” and these words distinguish his place in society. The frequency illustrates his reliance on law and his lack of trust in others based on the religious persecution he receives from Christian society. Finance also becomes his way of perceiving individuals. His equally frequent mention of “ducats” and “thou” conveys how often he associates money with people, and they become interchangeable to the extent where people cost money. For example, Shylock says to Antonio, “The pound of flesh/which I demand of him/is dearly bought” (4.1.2021).

     Stemming away from men, the Venetian Project Team carried interest in analyzing the position of women and what they most valued. As Jessica resides as one of the three female characters in Merchant of Venice, studying her graph generated prominent results. While she is Jewish, her most frequent mention of Lorenzo, a Christian, marks an allusion to Romeo and Juliet and foreshadows an element of tragedy to come. Quite evidently for Jessica, love for a significant other ranks higher than love for one’s parent, which explains her immediate, uninhibited escape from her father Shylock’s home. Jessica refers to the notion of love second to her mention of Lorenzo, and this conveys where her values lie. Romance is more important than filial piety and the combinations of “farewell,” “father,” “Lancelot,” and “night” solidifies her selfish nature.

     Apart from individual character analysis, the team’s next step was to use WordHoard to study and compare the tragic character of Shylock to other tragic characters across Shakespearian plays. WordHoard is a program that contains all of Spenser’s, Chaucer’s, and Shakespeare’s completed works, along with the Greek epics, and having this already-available database proved itself a valuable boon. This program underwent a lemmatization process, whereby enabling users to search for a word, as well as generating a list of where the word was used (in a specified text) and providing context for how it was used. “Lemma” is the most basic form of a word, therefore if an individual were to search for “take”, extended forms of the word such as “took”, “taken” or “taking” will display in the results. The creators of the site refer to this concept as “deep tagging”.

     The team studied the tragic characters of Hamlet, Othello, King Lear, and Macbeth by first running each character’s entire dialogue through WordHoard. A few of the team’s goals was to see if each character’s identity (especially Shylock’s Jewish one) distinguished itself in his word frequency and whether or not differences could be recognized by looking at the graphs. The team realized that by comparing each of the four tragic character’s graph to Shylock’s that Shylock shared either key personality traits with each character or was subjected to similar life-changing moments. Shylock and Hamlet dealt with loss of family through death and betrayal. Shylock and Othello were disparate from their fellow characters in terms of cultural identity. Each confronted unfaithfulness or infidelity with aggression, and the words for both characters construct arguments fueling their courses of action. Shylock and King Lear both accused their daughters of disloyalty and their lack of love, and Shylock and Macbeth adopted aggressive behaviors toward obtaining their goals.

     These character traits and societal factors are patterns that the project team discovered by comparing graphs. But not only did the team discover similarities, they uncovered major differences as well. Hamlet’s graph displays how he is prone to ruminations; his unrepressed thoughts stream through words such as “why” and “heaven”, which represent his aspiration to seek truth concerning his father’s murder. In Shylock’s graph, words such as “Christian” and “money” reflect how Shylock’s personal dilemma involves business and society, whereas Hamlet’s involves specific people: his uncle, mother, father, and himself. In Othello’s graph, his words convey that his issue concerns Desdemona, Iago, and Cassio, which contrasts to Shylock’s blatant opposition to the community. As for King Lear, he is a pagan that undergoes a distinctly Christian lesson of humility. His list of words describes the course that his internal character travels through; he initiates from a position of anger and loneliness, to insanity and confusion, and reaches selflessness and compassion. While King Lear undergoes moral lessons, he learns from then before dying a literal death, whereas Shylock does not learn, but rather grows increasingly distressed only to die a religious one. The team concluded that the major difference that separates Shylock from all other characters is his Jewish identity. Shylock’s words illustrate that his conflict with others stem from a vendetta against social identity, whereas all other characters react based on what others have said and done.

     While comparing tragic characters through word frequency generated fascinating results, the team wanted to perform the same study, but this time, based on another data-based approach. PieSpy is a tool for analyzing IRC (Internet Relay Chat) that was initially restricted to analyzing online instant messaging chats, but can now also generate a systematic model of characters within a specified text. PieSpy takes a “snapshot” of each scene and displays the relationship between each character. The program provides a visualization of lines- depending on whether they are bold, thin, long, or short- that connect character names to convey how strong or weak their relationships are. The project team employed two methods to observe PieSpy results. In the first method, members combined all snapshots, chronologically, into a video clip. This offered a means to study character pairings, groupings, isolations, as well as recognize relationship patterns. The second method dealt with focusing on each specific snapshot, and thereby presenting explanations of each scene that could further support or oppose what word frequency already provided.

     Within the first initial snapshots, the team realized that characters shared a tendency to cluster, and noticed how different themes within the play are addressed through specific individuals, pairs, and groups. One way this was made certain was how most of the pairings and groupings remain collected, as they initially were, throughout the majority of the play.

     In the first few scenes, Lancelot and Old Gobbo are paired, as are Portia and Nerissa, followed by the triangular group: Shylock, Antonio, and Bassannio. By observing these groups, the team noticed that themes strictly involved filial piety, friendship, and religion. Lancelot and Old Gobbo share a father-son relationship as they promote values of familial loyalty and love. Nerissa is both a serving maid and friend to Portia, and their relationship constantly conveys notions of companionship and trust. Antonio and Bassanio are passionate Christians, and while Shylock is a faithful Jew, these characters provoke one another steering all dialogue toward religion.

     As the team’s interest in studying Shylock and his position as “the tragic character” grew, the team used PieSpy to further study how Shylock’s character operated. The team noticed that the difference between Portia and Shylock was their stage position and connection to other characters within the same scenes. In one of the courtroom snapshots, Shylock is positioned the furthest from everyone and the only line that stems from him connects him to Portia. She stands in the center of this image, connected to all other characters present (Bassannio, Antonio, The Duke, and Gratiano). In this scene, Portia disguises herself as a male lawyer and although women were incapable of receiving equality with men during that time period, she manages to become the center of respect and authority amongst them. Shylock never experiences this and the disparity between his social mobility in comparison to Portia’s results in her favor as she is able to break through social barriers.

     PieSpy, along with Tagcrowd and WordHoard are programs that take a step away from close-reading and generate information resulting from quantitative analysis. As conventional as close-reading is to the modern literary critic, these data-based tools take textual comprehension and experience to a higher level. As PieSpy stages characters in proximities to one another based on what data yields, this form of resource offers an additional degree of knowledge that an individual— who simply reading a text— may not grasp. Tagcrowd and WordHoard alike offer irrefutable results that convey patterns that readers may also likely overlook. As the age of technology continues to expand and advance, one can only anticipate and imagine what the next level of analyzing text has in store.

Works Cited:

1. Cadinha, Ryan, Lim, Lillian, Moore, Kristina, Wong, Jason.The Venetian Project. Google Sites - Free websites and wikis. 13 Mar. 2009 .

2. "Full text- script of the play Merchant of Venice by William Shakespeare.” William Shakespeare. 11 Feb 2009.

3. Racism and Homophobia in the Merchant of Venice. ELH 70, no. 2 (summer 2003): 375-97. Feb 10 2009

4. Shakespeare, William. The Merchant of Venice. New York, New York: Penguin Group, 1999. O’Rourke, James.