Skip to main content
Engineering LibreTexts

8.2: TermCounter

  • Page ID
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    TermCounter is a class that represents a mapping from search terms to the number of times they appear in a page. Here is the first part of the class definition:

    public class TermCounter {
        private Map<String, Integer> map;
        private String label;
        public TermCounter(String label) {
            this.label = label;
   = new HashMap<String, Integer>();

    The instance variables are map, which contains the mapping from terms to counts, and label, which identifies the document the terms came from; we’ll use it to store URLs.

    To implement the mapping, I chose HashMap, which is the most commonly used Map. Coming up in a few chapters, you will see how it works and why it is a common choice.

    TermCounter provides put and get, which are defined like this:

    public void put(String term, int count) {
        map.put(term, count);
    public Integer get(String term) {
        Integer count = map.get(term);
        return count == null ? 0 : count;

    put is just a wrapper method; when you call put on a TermCounter, it calls put on the embedded map.

    On the other hand, get actually does some work. When you call get on a TermCounter, it calls get on the map, and then checks the result. If the term does not appear in the map, TermCount.get returns 0. Defining get this way makes it easier to write incrementTermCount, which takes a term and increases by one the counter associated with that term.

    public void incrementTermCount(String term) {
        put(term, get(term) + 1);

    If the term has not been seen before, get returns 0; we add 1, then use put to add a new key-value pair to the map. If the term is already in the map, we get the old count, add 1, and then store the new count, which replaces the old value.

    In addition, TermCounter provides these other methods to help with indexing Web pages:

    public void processElements(Elements paragraphs) {
        for (Node node: paragraphs) {
    public void processTree(Node root) {
        for (Node node: new WikiNodeIterable(root)) {
            if (node instanceof TextNode) {
                processText(((TextNode) node).text());
    public void processText(String text) {
        String[] array = text.replaceAll("\\pP", " ").toLowerCase().split("\\s+");
        for (int i=0; i<array.length; i++) {
            String term = array[i];
    • processElements takes an Elements object, which is a collection of jsoup Element objects. It iterates through the collection and calls processTree on each.
    • processTree takes a jsoup Node that represents the root of a DOM tree. It iterates through the tree to find the nodes that contain text; then it extracts the text and passes it to processText.
    • processText takes a String that contains words, spaces, punctuation, etc. It removes punctuation characters by replacing them with spaces, converts the remaining letters to lowercase, then splits the text into words. Then it loops through the words it found and calls incrementTermCount on each. The replaceAll and split methods take regular expressions as parameters; you can read more about them at

    Finally, here’s an example that demonstrates how TermCounter is used:

    String url = "";
    WikiFetcher wf = new WikiFetcher();
    Elements paragraphs = wf.fetchWikipedia(url);
    TermCounter counter = new TermCounter(url);

    This example uses a WikiFetcher to download a page from Wikipedia and parse the main text. Then it creates a TermCounter and uses it to count the words in the page.

    In the next section, you’ll have a chance to run this code and test your under- standing by filling in a missing method.

    This page titled 8.2: TermCounter is shared under a CC BY-NC-SA 3.0 license and was authored, remixed, and/or curated by Allen B. Downey (Green Tea Press) .

    • Was this article helpful?