Lab Session: Digital Humanities

Lab Session: Digital Humanities

- In this blog, we have to share our experiences using these three tools and our learning outcomes. This activity was assigned by Dilip Barad Sir 

1. Human or Computer? – Poem Test

In the beginning, we reflected on the long-standing debate: Can machines write poetry? To test this idea, we were given a poem and asked to decide whether it was written by a human or generated by a computer. This activity made me think deeply about creativity, language, and the subtle differences between natural and artificial expression. It was surprising to see how closely machine-generated text can resemble human creativity, but also how certain emotional depths and nuances often remain distinct.

2. Voyant Tools:

Trends:

📊 Graph Structure and Design X-Axis (Documents): 

Each tick represents a distinct document or segment within the corpus. These could be literary texts, chapters, or other textual units.

Y-Axis (Relative Frequency): Indicates how often each word appears relative to the total word count in each document, allowing for normalized comparison.

Color Coding: Blue for “good”

Pink for “bad”

Green for “evil”

The color distinction helps track semantic trends and emotional valence across the corpus.

🔍 Lexical Trends and Semantic Implications:

1. “Good” (Blue Line) Dominant and Consistent: The blue line generally stays above the others, suggesting that “good” is the most frequently used term across the corpus.

Interpretation: This could reflect a corpus with moral or ethical discourse, possibly religious, philosophical, or literary texts emphasizing virtue, redemption, or idealism.

2. “Bad” (Pink Line) Moderate Frequency with Fluctuations: The pink line shows variability, sometimes nearing the frequency of “good,” but never surpassing it.

Interpretation: “Bad” may be used in more colloquial or evaluative contexts, indicating moral judgment or character flaws. Its presence suggests a nuanced moral landscape rather than binary opposition.

3. “Evil” (Green Line) Least Frequent but Spiking in Specific Texts: The green line is generally low but shows sharp spikes in certain documents.

Interpretation: “Evil” is likely reserved for moments of heightened moral intensity perhaps in texts dealing with villainy, sin, or metaphysical conflict. Its selective use implies a more dramatic or theological framing.

📚 Possible Corpus Characteristics:

Given the moral polarity of the selected terms, the corpus might include: Religious texts (e.g., sermons, scripture)

Literary works with moral themes (e.g., Victorian novels, Gothic fiction)

Philosophical or ethical treatises

Modernist or postcolonial texts exploring moral ambiguity

The presence of “evil” in spikes could suggest texts that dramatize conflict or critique systems of power, aligning with your interest in postcolonial and modernist literature.

Stylistic and Thematic Insights Moral Lexicon as Narrative Device:

The interplay between “good,” “bad,” and “evil” may reflect how authors construct moral binaries or challenge them.

Stylistic Register: “Good” and “bad” are simpler, more universal terms, while “evil” carries heavier connotations often theological, mythic, or ideological.

Temporal or Genre Shifts: If the documents are chronologically ordered, the graph might reveal how moral language evolves over time or across genres.

✨ Reflective Angle for Literary Analysis:

This graph could be a springboard for exploring: Characterization: How often are protagonists described as “good” vs antagonists as “evil”?

Narrative Voice: Does the narrator use “bad” more than “evil,” suggesting a more humanistic or realistic tone?

Ideological Framing: Are spikes in “evil” tied to colonial discourse, religious dogma, or political critique?

Cirrus:

StreamGragh:

This activity introduced us to Voyant, a text analysis platform. This tool allowed me to visualize word frequency, track themes, and examine language patterns in a text. Creating word clouds, StreamGragh, Cirrus and trend graphs made the analysis more engaging, and it highlighted how certain terms or ideas dominate across passages.

3. CLiC:

In this activity we will make use of the concordance and annotation tools to explore two themes in Arthur Conan Doyle’s 1890 novel featuring Sherlock Holmes, The Sign of the Four. We will look at justice and the role of women.

Activity 11.1 Justice:

Go to the CLiC Concordance tab (http://clic.bham.ac.uk/concordance

).

Select The Sign of the Four in the “Search the Corpora” box. You can start typing the book title and CLiC will autocomplete, or you can scroll down to find it in the section entitled “ArTs – Additional Requested Texts.”

Select the subset “Quotes.”

Under “Search for terms,” type the word justice.

Who speaks about justice in the novel? Go through the concordance lines and check the speaker. (Remember – you can click on the graphic in the column “In bk.” on the right-hand side in order to go to the concordance line in the full context of the book itself.)

• Learning Outcomes:

Through this method, we can identify which characters bring up justice and in what situations whether it is Holmes reflecting on the fairness of outcomes, Watson commenting on morality, or villains framing justice in terms of revenge. Tagging each concordance line by speaker makes it easier to notice patterns in how justice is voiced. This technique is especially useful with larger datasets or when multiple speakers are involved.