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Adventures of Sherlock Holmes: Sentiment Analysis.
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Introduction
The famous Sherlock Holmes quote, “Data! data! data!” from The Copper Beeches perfectly encapsulates the essence of both detective work and data analysis. Holmes’ relentless pursuit of every detail closely mirrors the approach of modern data analysts, who understand that conclusions drawn without solid data are mere conjecture. Just as Holmes systematically gathered clues, analysed them from different perspectives, and tested hypotheses to arrive at the truth, today’s analysts follow similar processes when investigating complex data-driven problems. This project draws a parallel between Holmes’ detective methods and modern data analysis techniques by visualising and interpreting data from The Adventures of Sherlock Holmes.
“Data! data! data!” he cried, impatiently. “I can’t make bricks without clay.”
The above quote comes from one of my favourite Sherlock Holmes stories, The Copper Beeches. In this single outburst, Holmes captures a principle that resonates deeply with today’s data analysts: without data, conclusions are mere speculation. Data is the bedrock of any investigation. Without sufficient data, the route to solving a problem or answering a question is clouded with uncertainty.
Sherlock Holmes, the iconic fictional detective, thrived on difficult cases, relishing the challenge of pitting his wits against the criminal mind.
His methods of detection:
Examining crime scenes.
Interrogating witnesses.
Evaluating motives.
Closely parallel how a data analyst approaches a complex problem today. By carefully collecting and interpreting data, Holmes was able to unravel mysteries that seemed impenetrable at first glance.
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Data Collection: Gathering Evidence Holmes’s meticulous approach to data collection mirrors the first stage of data analysis. Just as Holmes would scrutinise a crime scene for every detail; whether it be a footprint, a discarded note, or a peculiar smell. Data analysts seek to gather as much relevant data as possible. Just as incomplete or biased data can skew results in modern analysis, Holmes understood that every clue mattered. Overlooking a small piece of information could compromise the entire investigation.
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Data Quality: “I can’t make bricks without clay.” This quote is more than just a witty remark, it highlights the importance of having the right data. In the same way that substandard materials result in poor construction, incomplete or inaccurate data leads to unreliable analysis. Today’s analysts face similar issues: they must assess data integrity, clean noisy datasets, and ensure they’re working with accurate information before drawing conclusions. Holmes, in his time, would painstakingly verify each clue, ensuring that he was not misled by false leads.
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Data Analysis: Considering Multiple Perspectives Holmes’s genius lay not just in gathering data, but in the way he analysed it. He would often examine a problem from multiple angles, revisiting clues with fresh perspectives to see what others might have missed. In modern data analysis, this approach is akin to using different models, visualisations, and analytical methods to interpret the same dataset. Analysts explore data from multiple viewpoints, testing different hypotheses, and applying various algorithms to see which provides the most plausible insight.
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Hypothesis Testing: Eliminate the Improbable One of Holmes’s guiding principles was: “When you have eliminated the impossible, whatever remains, however improbable, must be the truth.” This mirrors the process of hypothesis testing in data analysis. Analysts might begin with several competing theories about what the data suggests. By testing these hypotheses, ruling out those that are contradicted by the data, they zero in on the most likely explanation. For both Holmes and today’s data analysts, the process of elimination is crucial to arriving at the correct answer.
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Insight and Conclusion: The Final Deduction After piecing together all the clues, Holmes would reveal his conclusion, often leaving his audience in awe at how the seemingly unrelated pieces of data fit together. Similarly, data analysts must present their findings clearly and compellingly, translating raw data into actionable insights. The ability to connect the dots and tell a coherent story from the data is what transforms analysis into impactful decision-making.
In summary, the methods Sherlock Holmes employed were gathering data meticulously, testing multiple angles, and drawing conclusions through careful analysis. Are strikingly similar to the techniques used by modern data analysts. Just as Holmes required high-quality data and a structured approach to solve crimes, today’s data analysts rely on well-prepared data and methodical analysis to provide insights. Whether you’re cracking a case or uncovering business trends, the detective mindset remains a timeless guide.
Visualisations: ADVENTURES_OF_SHERLOCK_HOLMES.csv
The following visualisations model The Project Gutenberg ebook - The Adventures of Sherlock Holmes:
- Word Count: This measures the frequency of each word in the text, highlighting commonly used words.
- Sentiment Analysis: Bing (count and percentage): Classifies words as positive or negative, showing their count and percentage. NRC (count and percentage): Categorises words across eight emotions (e.g., joy, anger) and sentiment (positive/negative), showing their count and percentage.
- Word Cloud: A visual representation of word frequency, where more frequent words are displayed larger.
- Topic Modelling (x4): Uses methods like LDA (Latent Dirichlet Allocation) to extract four main topics from the text, each grouping related words.
- Flesch-Kincaid Readability Score: Assesses the text's reading difficulty, with a score indicating the required education level to understand it.
- Common Bigrams: Identifies frequent pairs of consecutive words (bigrams), highlighting commonly occurring word combinations. These methods provide insights into word usage, sentiment, topics, readability, and key phrases in the text.
The Bing Lexicon focuses on a more binary assessment of sentiment, leading to a simpler but less nuanced analysis. The NRC Lexicon, on the other hand, offers a richer emotional landscape by breaking down sentiment into multiple categories, providing a more detailed understanding of the text's emotional tone. This difference in approach is why the two graphs present differing sentiment analysis results.
Across these topics, key terms like Holmes, door, matter, and time are consistently prevalent, underscoring recurring themes in detective stories: movement (doors, houses), critical timing, and observation. While each topic has unique elements (like references to women in Topic 3 or timing in Topic 2), they collectively paint a picture of the classic detective narrative centred around mystery, discovery, and interaction with various characters.
In the English schooling system, a Flesch-Kincaid readability score of 6.087 would roughly correspond to the reading ability expected of students in Year 7 or Year 8 (ages 11-13), which is early Key Stage 3. The text’s level of complexity is suitable for this age group in terms of vocabulary and sentence structure. However, the plot complexity and some archaic language may still challenge readers slightly above that level. This score indicates that while it’s a classic literary work, it’s still quite readable for a broad audience.
A Markdown document with the R code for the above visualisations. link
Conclusion:
This project demonstrates how fundamental principles in Sherlock Holmes’ detective work align closely with data analysis techniques. Whether gathering comprehensive data, ensuring data quality, analysing from multiple perspectives, or rigorously testing hypotheses, both disciplines require careful attention to detail and methodical thinking. The visualisations provided, from word counts and sentiment analysis to topic modelling and readability assessments, illustrate how classic literature can be explored through a data-driven lens. Just as Holmes relied on meticulous analysis to solve mysteries, data analysts rely on structured processes and insightful interpretations to uncover hidden patterns and derive meaningful conclusions.
License
CC0
Original Data Source: Adventures of Sherlock Holmes: Sentiment Analysis.
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