As part of Lab Session 1: Digital Humanities, assigned by Prof. Dr. Dilip P. Barad, Head of the Department of English, Maharaja Krishnakumarsinhji Bhavnagar University (MKBU), I explored how digital tools can offer new ways of reading, analysing, and understanding literature. Through this lab, I examined the question “Can machines write poems?”, took the Human or Machine poetry test, explored CLiC and its Activity Book through the study of education, women, and governesses in nineteenth-century literature, and analysed Mary Shelley’s Frankenstein or The Modern Prometheus using Voyant Tools and its visualisations. This blog brings together my experiences, observations, screenshots, and learning outcomes from these activities, showing how Digital Humanities connects literary study with technology, corpus analysis, and data visualisation. Along with the blog, I have also included an infographic and slide deck summarising the overall lab experience. Here is the professor’s blog link for the background reading: Click here
Lab Session 1: Digital Humanities | From Machine Poetry to Digital Text Analysis: Exploring CLiC and Voyant Tools
Here is the Infographic of this whole blog:
Here is the Slide Deck of this whole blog:
Introduction
Digital Humanities, for me, is a way of bringing technology and literary studies together. Traditionally, we study literature by reading, interpreting, and discussing texts, but Digital Humanities allows us to explore literature with the help of digital tools, data, and computational methods. This makes the study of language and literature more interactive and helps us notice patterns and details that may not always be visible through traditional reading alone.
Lab Session 1: Digital Humanities was important for me because it gave me an opportunity to experience these methods practically rather than only learning about them theoretically. During this lab session, I explored the question of whether machines can write poetry, took a Human or Computer poetry test, and worked with digital tools such as CLiC, the CLiC Activity Book, Voyant Tools, and Orange. Each activity introduced me to a different way of examining language, literature, and digital texts.
Before starting the lab, I expected the activities to be mainly technical and perhaps difficult because I had limited experience with digital text-analysis tools. However, I was also curious to see how technology could be used in English literature. As I began working with these tools, I realized that Digital Humanities is not about replacing traditional literary study. Instead, it provides new methods of reading, analysing, and understanding texts. This lab session therefore became an opportunity for me to connect my knowledge of literature with digital technology and to develop a more practical understanding of Digital Humanities.
What If Machines Write Poems?
Starting this lab session with Professor Dilip Barad’s blog post, “What if Machines Write Poems?”, immediately challenged how I think about creative writing. As a student deeply immersed in literature, I’ve always believed that poetry is the ultimate expression of human emotion something born out of personal suffering, love, memory, and lived experience. The idea that a string of code could replicate that felt almost unsettling.
Watching Oscar Schwartz's TED Talk about the "Turing Test for Poetry" made me realize that computers don’t actually understand the meaning of the words they use. Algorithms like those mentioned in the reading take a source text like the works of William Blake or Emily Dickinson analyze its linguistic patterns, and regenerate new lines that mimic that exact structure. The machine isn't feeling anything; it is simply functioning as a mirror, reflecting back the language structures we feed into it.
To test this out myself, I tried out the interactive poetry quiz (Human or Machine: Can You Tell Who Wrote These Poems?). Going into it, I thought I would easily spot the machine-generated poems by looking for robotic phrasing or awkward rhythms. Instead, I found the test genuinely difficult. Some of the algorithmic verses flowed with a haunting, smooth rhythm that completely fooled my instincts, while some human poems felt experimental enough to seem computer-generated.
Exploring CLiC – Education, Women and Governesses in the 19th Century
CLiC (Corpus Linguistics in Context) is a digital tool developed through the CLiC Dickens project for exploring literary texts with computer-assisted methods. It allows readers to search for particular words and phrases and examine them in their surrounding contexts. In this lab session, I explored Chapter 15 of the CLiC Activity Book, “Education, women and governesses in the 19th century.” The activities took me from a general understanding of the governess in nineteenth-century society to searching literary texts and comparing different examples through CLiC.
1 Understanding the Governess – Activity 15.1
British Library video – “The Governess”
I began by watching the British Library video “The Governess.” From the video, I understood that a governess was generally an educated woman who lived with a family and was responsible mainly for educating their children. She could teach basic subjects such as reading, writing and arithmetic, as well as subjects considered important for girls’ education, such as languages, music and other accomplishments.
What I found most interesting was the governess’s uncertain social position. She was neither fully a member of the family nor simply an ordinary servant. She worked in the household but was expected to maintain the manners and status of a respectable middle-class woman. This could make her socially isolated and create tension between her and both the family and the servants.
The video also helped me understand why the governess became such an important figure in nineteenth-century literature. Characters such as Jane Eyre in Charlotte Brontë’s Jane Eyre and Rebecca Sharp in William Thackeray’s Vanity Fair show how the governess could become a central character who was respectable but still independent enough to experience difficulties and adventures outside her family home.
2 Exploring Austen’s Governesses – Activity 15.2
After the introductory activity, I moved to CLiC Concordance. I selected Jane Austen’s novels, chose the “All text” subset, and searched for the word “governess.” CLiC then displayed concordance lines containing the word and showed the surrounding context. I also used the “In bk.” option to open the original passage in the novel.
This activity helped me see how a simple keyword search can provide a quick overview of a literary theme. Instead of searching through several novels manually, CLiC brought together the relevant occurrences of “governess” and allowed me to compare how the term is used in different contexts.
3 Social Status of Governesses – Activity 15.3
The passage gave me a clear picture of different attitudes towards education and governesses. Lady Catherine assumes that a proper family should have a governess and is surprised that Elizabeth and her sisters were brought up without one. She considers regular instruction by a governess an important part of education. Elizabeth, however, explains that although her family may have been less formally educated than some others, they were encouraged to read and had access to the necessary teachers.
This contrast shows that a governess was connected not only with education but also with social class, family expectations and ideas of respectable upbringing. Lady Catherine’s reaction also reveals her strong belief in social conventions and formal education.
The activity also made me pay attention to who is speaking and how the narrator presents the character. Most of the passage is given directly through Lady Catherine’s speech, which allows her opinions and personality to become visible through her own words. This helped me understand why the Activity Book draws attention to narration and Free Indirect Discourse in Austen’s writing.
4 Children’s Feelings about Governesses – Activity 15.4
I then explored Frances Hodgson Burnett’s The Secret Garden through the ChiLit – 19th Century Children’s Literature Corpus. I selected the novel, searched for “governess” in All text, and examined the occurrences through their contexts.
One of the most interesting passages is Mary’s response:
“I don’t want a governess,” said Mary sharply.
The surrounding passage shows that the adults think a governess would be useful because Mary is growing up without her mother and needs someone to educate and care for her. Mary, however, does not want a governess. This creates an interesting contrast between adult expectations and a child’s own feelings.
Through this activity, I understood that the word “governess” can reveal much more than a profession. It can lead us to questions about childhood, education, care, discipline and family relationships.
5 Broadening the Exploration – Activity 15.5
In the final part, I broadened the search beyond individual novels. I selected a corpus from the options given in the Activity Book Dickens’s Novels (DNov), the 19th Century Reference Corpus (19C), or the 19th Century Children’s Literature Corpus (ChiLit) and ran a concordance search for “governess.”
I then examined the concordance lines and looked for examples connected with the social position of governesses, the children they cared for, and their social background. This activity helped me move from studying one particular passage to looking for broader patterns across nineteenth-century literature.
The concordance made it possible to see how the role of a governess could be associated with education and childcare while also reflecting issues of class, gender and social position. The examples also encouraged me to think about why educated women might choose governess work and why this occupation could place them in an uncertain position within the household.
📸 Screenshot: Selected corpus
📸 Screenshot: “governess” concordance search
📸 Screenshot: Interesting examples from the results
Exploring Voyant Tools
For the next part of the Digital Humanities Lab, I explored Voyant Tools, a web-based platform for analysing and visualising literary texts. For my activity, I selected Mary Shelley’s Frankenstein or The Modern Prometheus, using the 1888 edition published by George Routledge and Sons. Instead of reading the novel only through traditional close reading, I used Voyant to examine the text through different visualisations. I explored DreamScape, Loom, Trends, and Bubbles, and each visualisation presented the novel from a different perspective.
🔗 Voyant Tools: https://voyant.lincsproject.ca/
1. DreamScape: Mapping the World of Frankenstein
My first exploration was DreamScape. This visualisation identifies place names from the text and represents them geographically on a world map. The map showed a wide range of locations connected with the novel, including places in Europe and North America. Seeing these locations together gave me a visual sense of how widely the narrative moves across geographical spaces.
The map was particularly interesting because Frankenstein is not limited to one setting. The story moves through different countries and regions, and DreamScape made this movement visible in a single image. It helped me think about the novel not simply as a story of one creature and one scientist, but also as a narrative involving travel, displacement, pursuit, and movement across places.
2. Loom: Looking at Term Distribution
I next explored Loom, which represents the distribution of terms across different parts of the text through interconnected lines. At first, the visualisation looked complex because many words were represented simultaneously. However, it helped me understand that word usage changes from one section of the novel to another.
The flowing lines show that some terms appear more strongly in particular sections while others remain more evenly distributed. This made me realise that vocabulary is not equally spread throughout a narrative. Certain words become more prominent at particular moments depending on the development of the story and its themes.
What I found most interesting was that the visualisation turns something abstract the movement of words through a long novel into something that can be seen. Instead of simply knowing that a word occurs many times, I could observe how its distribution changes across the text.
3. Trends: Tracking Important Words Across the Novel
The Trends visualisation was one of the most useful parts of my experiment. It shows how the frequency of selected words changes across different segments of the text. In my graph, I explored words including “time,” “shall,” “saw,” “said,” “night,” “man,” “life,” “Frankenstein,” “father,” and “eyes.”
The graph shows that these words do not remain at the same level throughout the novel. Their frequencies rise and fall across the ten segments. For example, “man” shows particularly noticeable peaks in some sections, while “frankenstein” also changes in frequency as the narrative progresses. Other terms such as “life,” “father,” “night,” and “eyes” also show different patterns across the text.
This visualisation helped me understand that word frequency can reflect changes in the narrative. The prominence of words connected with life, human identity, family, perception, and time can encourage further questions about the themes and concerns of the novel. However, the graph itself does not give the final interpretation; it provides evidence that can guide a closer reading of the relevant passages.
4. Bubbles: Exploring Prominent Words
I also explored the Bubbles visualisation. In this view, frequently occurring words are represented by circles, with larger bubbles indicating more prominent terms. In my visualisation, words such as “life,” “time,” “thought,” “house,” “mind,” “hours,” “far,” and “idea” appeared prominently.
The word “life” was especially noticeable because of its large bubble and highlighted position. Other words such as “mind,” “thought,” “time,” and “death” also stood out. These words immediately drew my attention to some of the central concerns of Frankenstein: the meaning of life, human knowledge, thought, mortality, and the consequences of scientific ambition.
What I found valuable about Bubbles was that it provided a quick visual overview of the vocabulary of the novel. At the same time, it reminded me that frequency alone does not determine meaning. A frequently occurring word still needs to be studied in its context before making a literary interpretation.
What I Learned from Voyant Tools
My experience with Voyant Tools showed me that a literary text can be explored through both close reading and digital analysis. The different visualisations helped me see aspects of Frankenstein that are difficult to notice immediately while reading the novel from beginning to end.
DreamScape helped me visualise the geographical movement of the narrative. Loom showed me how vocabulary is distributed across the text. Trends helped me track changes in the frequency of selected words, while Bubbles gave me a quick visual picture of prominent terms.
The main lesson I learned is that these visualisations do not replace literary interpretation. Instead, they provide evidence, patterns, and new questions that can support close reading. Through this activity, I understood more clearly how Digital Humanities can turn a large literary text into something that can be explored through data as well as interpretation.
For me, Voyant Tools made the reading of Frankenstein more interactive. It encouraged me to look beyond individual passages and think about how words, themes, places, and patterns develop across the whole novel. This experience showed me that technology can provide another valuable method for understanding literature.
- Barad, Dilip. "What if Machines Write Poems." Dilip Barad | Teacher Blog, 21 Mar. 2017, blog.dilipbarad.com/
2017/03/what-if-machines- write-poems.html. - Mahlberg, Michaela, et al. CLiC: Corpus Linguistics in Context. University of Birmingham, clic.bham.ac.uk. Accessed 16 Aug. 2026.
- Schwartz, Oscar. "Can a computer write poetry? | Oscar Schwartz." YouTube, uploaded by TED, 10 Feb. 2016, youtu.be/UpkAqPEcMyE?si=
YUhKv1pXK9xl9sbh. - Sinclair, Stéfan, and Geoffrey Rockwell. Voyant Tools. 2016, beta.voyant-tools.org. Accessed 16 Aug. 2026.
