Last Updated on May 6, 2023 by mishou
Tell me who wrote this,
Mr. Utterson the lawyer was a man of a rugged countenance that was never lighted by a smile; cold, scanty and embarrassed in discourse; backward in sentiment; lean, long, dusty, dreary and yet somehow lovable..
The passage you provided is the opening description of Mr. Utterson, a character from the novella “Strange Case of Dr. Jekyll and Mr. Hyde” written by Robert Louis Stevenson.
I want to analyse the literal style of “Strange Case of Dr. Jekyll and Mr. Hyde” using Python. Do you have any suggestion?
Certainly! Analyzing the literal style of “Strange Case of Dr. Jekyll and Mr. Hyde” using Python can involve several steps. Here are some suggestions on how you can approach this analysis:
- Text Preprocessing: Load the text of the novella into Python and preprocess it. Remove any unnecessary punctuation, convert the text to lowercase, and split it into individual words or tokens. You can use Python libraries such as NLTK (Natural Language Toolkit) or spaCy for this task.
- Word Frequency Analysis: Calculate the frequency of each word in the text. This will help you understand the most commonly used words in the novella. Python’s “collections.Counter” or NLTK’s “FreqDist” can be helpful for this analysis.
- Sentence Length Analysis: Analyze the length of sentences in the text. Calculate the average sentence length, identify long or short sentences, and observe any patterns. You can use NLTK or regular expressions to tokenize sentences and calculate their lengths.
- Part-of-Speech (POS) Tagging: Assign part-of-speech tags to each word in the text. This will help you identify the types of words used by Stevenson and their distribution. NLTK and spaCy offer pre-trained models for POS tagging in English.
- Stylistic Figures: Look for specific stylistic figures used in the novella, such as metaphors, similes, personification, or alliteration. You can use regular expressions or Python libraries like spaCy to identify these figures based on patterns or linguistic cues.
- Sentiment Analysis: Analyze the sentiment expressed in the text. Determine whether the overall tone is positive, negative, or neutral. Python libraries like NLTK or VADER (Valence Aware Dictionary and sEntiment Reasoner) can help with sentiment analysis.
- Visualizations: Visualize the results of your analysis using Python libraries like Matplotlib, seaborn, or word clouds. Create plots or word clouds to represent word frequencies, sentence lengths, or sentiment distribution.
Remember, these are just suggestions, and you can adapt or expand upon them based on your specific requirements and interests. Python provides a wide range of libraries and tools for natural language processing (NLP) that can assist you in analyzing the literal style of “Strange Case of Dr. Jekyll and Mr. Hyde” or any other text.
Show the Python code for Word Frequency Analysis on “preprocessed_text”.
And I asked ChatGPT to show related Python code and run them on Google Colaboratory. Though they are not completed yet, the results are here on Google Colaboratory: