(Text Analysis) The availability of computers with string-manipulation capabilities has resulted in some rather interesting approaches to analyzing the writings of great authors. Much attention has been focused on whether William Shakespeare ever lived. Some scholars believe there's substantial evidence indicating that Christopher Marlowe actually penned the masterpieces attributed to Shakespeare. Researchers have used computers to find similarities in the writings of these two authors. This exercise examines three methods for analyzing texts with a computer. a) Write an application that reads a line of text from the keyboard and prints a table indicating the number of occurrences of each letter of the alphabet in the text. For example, the phrase To be, or not to be: that is the question: contains one "a," two "b's," no "c's," and so on. b) Write an application that reads a line of text and prints a table indicating the number of one-letter words, two-letter words, three-letter words, and so on, appearing in the text. For example, Fig. 16.25 shows the counts for the phrase Whether 'tis nobler in the mind to suffer $$\begin{array}{ll}\text { Word length } & \text { Occurrences } \\\1 & 0 \\\2 & 2 \\\3 & 1 \\ 4 & 2 \text { (including 'tis) } \\\5 & 0 \\\6 & 2 \\\7 & 1\end{array}$$ Fig. \(16.25 \quad\) Word-length counts for the string "Whether 'tis nobler in the mind to suffer". c) Write an application that reads a line of text and prints a table indicating the number of occurrences of each different word in the text. The application should include the words in the table in the same order in which they appear in the text. For example, the lines To be, or not to be: that is the question: Whether 'tis nobler in the mind to suffer contain the word "to" three times, the word "be" two times, the word "or" once, etc.

Step by step solution

01

Defining the Problem for Letter Frequency Analysis

To analyze the frequency of each letter in a given text, we need an application that will take a string input and count the occurrences of each letter of the alphabet. This requires setting up a data structure (like an array or a dictionary) to hold the count of each letter and then iterating over the string updating the letter count for each character.

02

Creating the Framework for Letter Frequency Analysis

The application should initialize a data structure with keys for each letter of the alphabet set to 0. Then, iterate over each character in the input string, convert the character to lowercase to ensure case insensitivity, and if the character is a letter, increment its corresponding count in the data structure.

03

Outputting the Results for Letter Frequency Analysis

Once the counting is complete, the application should iterate over the data structure and print out the letters and their associated counts in a tabular format, showing only the letters that have a non-zero count.

04

Defining the Problem for Word Length Frequency Analysis

The challenge here is to count the frequency of word lengths within a text. We will need to break the text into words, determine the length of each word, and count the occurrences of each word length.

05

Creating the Framework for Word Length Frequency Analysis

Initialize a data structure to hold the count of word lengths. With the text string provided, use a method to split the string into individual words. Loop through these words, calculate their lengths, and for each length, increment its count in the data structure.

06

Outputting the Results for Word Length Frequency Analysis

After counting, print the results in a tabular format showing the word length and the corresponding number of occurrences for each word length.

07

Defining the Problem for Individual Word Frequency Analysis

In this task, we aim to count the occurrences of each distinct word in a text. A word is a string of characters delimited by whitespace or punctuation.

08

Creating the Framework for Individual Word Frequency Analysis

Develop a data structure (such as a dictionary) to store and update the word count. Normalize the text by converting it to lowercase and remove any punctuation. Split the text into words and for each word, increment its count in the data structure.

09

Outputting the Results for Individual Word Frequency Analysis

Iterate over the data structure to print each word followed by the number of times it appears in the text, maintaining the order in which they appear in the source text.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

String Manipulation in Java

String manipulation is a fundamental aspect of text analysis and a critical skill when working with textual data in Java. It involves various operations such as adding, removing, substituting, or altering strings within your program. In Java, the String class provides numerous methods for these tasks such as substring(), replace(), toLowerCase(), and toUpperCase(). Furthermore, when analyzing texts, one might need to split a string into words using the split() method, which divides the string around matches of the given regular expression. For the exercises, we utilized these methods to preprocess the text by converting all letters to lowercase to ensure a case-insensitive analysis, and by using regular expressions to handle punctuation.

When working with mutable strings, Java's StringBuilder or StringBuffer can be particularly useful as they allow changes without creating new string objects. Students should therefore be comfortable with these string manipulation techniques to effectively handle text analysis tasks in Java.

Letter Frequency Analysis

Letter frequency analysis is all about quantifying the appearance of each letter in a piece of text. This is a common task in cryptography, linguistics, and text analysis tasks like the one in our exercise. To carry out this analysis in Java, we start by creating a data structure, often an array or a HashMap, to store the counts of each letter. Subsequently, we iterate through the input string, updating our structure accordingly. It's crucial to normalize the string, usually by converting it to a single case, so that 'A' and 'a' are not counted separately.

In our exercise, we used a HashMap where each key-value pair corresponds to a letter and its count. The elegance of using a HashMap lies in its ability to dynamically grow and its provision of a default value (typically 0) for each letter, simplifying the counting process. Only non-zero counts are then displayed in a table format, making it clear which letters are present in the input and their frequency.

Word Length Frequency

Word length frequency is an insightful metric in text analysis; it examines the distribution of words based on their length. In Java, after splitting the text into individual words using the split() method, we map each word length to its occurrence in a similar manner to letter frequency analysis.

In our step-by-step solution, we initialized a HashMap to record the length frequencies. When splitting the string, spaces and punctuation were used as delimiters to ensure that 'words' encapsulated by quotes or followed by commas were not miscounted. Then, for every word, its length was calculated and used to update the frequency in our map. Finally, we printed a table displaying how many times each word length appeared in the text. This analysis can reveal patterns in word usage and inform linguistic aspects of the writing style at hand.

Individual Word Frequency

Analyzing the frequency of individual words provides a deeper understanding of the text's composition. This technique is widely used in natural language processing for tasks like keyword extraction and theme analysis.

In Java, the exercise was tackled by first normalizing the input text: converting it to lowercase and stripping away punctuation. This standardization is critical to accurately count words without duplicates caused by casing or attached punctuation. A LinkedHashMap was used for storing word counts because it preserves the insertion order, allowing us to print occurrences in the order words appear in the text. This method of tracking not just the frequency, but also the order of words adds a layer of comprehension regarding the structure and nuances of the original text.