Text frequency or wordlist analysis in Hamlet II

In the previous article, introductory aspects and the interface of Hamlet II were presented. Text analysis, or text frequency analysis, is an important and common text-based analysis using Wordlist. In this analysis, the transcript or the text file is assessed for occurrence or repetition or frequency of words. This is known as the wordlist analysis in Hamlet II. Wordlist creates a list of all the most repetitive words in a text file thereby showing the importance of the reoccurring words.

Text analysis is the technique of assessing texts from a draft or mainly a transcript. Text analysis also helps to create an overview of the most common words and show relevance to a research study. Text frequency analysis is also known as wordlist.

Steps for performing wordlist analysis in Hamlet II

Follow the below steps for performing wordlist analysis using Hamlet II.

• Step 1: Transcribe interview responses in a Word document and convert it from .docx/.doc to .txt format.
• Step 2: Now copy the .txt file format to the source folder of Hamlet II and paste the file. Source folder is the folder in C-drive where the Hamlet II software is installed.
• Step 3: Open a file in Hamlet II using the following steps:
  1. Click on ‘File’
  2. Click on ‘Select’
  3. Select the appropriate file and click on ‘Open’.

The image below shows the process of step 3.

Now, Hamlet II will import the .txt file as shown in image below.

• Step 4: To perform the wordlist analysis in Hamlet, perform the following steps.

1. On the main toolbar or taskbar click on ‘Tools’.
2. Click on ‘Wordlist’.
3. In the new window, select the file.
4. Click on ‘create a word list’.

The figure below shows the entire step 4 (figure below).

• Step 5: A popup box for confirmation of the analysis will appear. These pop-up boxes usually ask if numerical are to be ignored for wordlist analysis. Click on ‘Yes’ (figure below).

The software will tabulate the results from the frequency analysis of word list (figure below).

• Step 6: Close the popup box and click ‘Yes’ to save the wordlist in the warning popup box.
• Step 7: Save the output in .LST file extension.

Creating a stop-list

The stop-list popup box will appear as shown in the image below.

• Step 8: Now, either select from the list of stop-lists or make a new one from the stoplist_template.stp
• Step 9: While creating one, choose any file and after a couple of popup boxes (ignore them, however, read them first) the stop-list entries appear in the list as shown in the figure below.

Wordlist using a stop-list

You can either list the words in a text and save them, or arrange them chronologically. Arranging them chronologically means lemmatization. In this process, arrange and remove unwanted words properly with the utilization of a vocabulary and morphological analysis of words.

• Step 10: On using the stop-list the result from wordlist analysis differs. Stop-list creates a list of words to exclude during wordlist assessment (figure below).
  

The figure below presents the result of wordlist after applying the stop-list feature. The words in the stop-list are ignored while testing wordlist. The list of stop-list comprised of words like; a, an, are, as, be, by, does, for, has, have, if, in, is, it, of, that, the, this, to, was, what, which, and whose. The stop-list helped in testing only the most important words needed from the text file.

Interpreting the results from Wordlist in textual analysis

The highest frequency of wordlist from the transcript, ‘tourism’ with 117 counts followed by ‘medical’ with 112 counts. They shared a combined frequency and re-occurrence of 3.2%, indicating the topic of ‘medical tourism’ or ‘tourism in the medical sector’. In addition, other important words assessed in the assessment were ‘patients’ (95), ‘hospital’ (93), and ‘India’ (64).

In case, the interpretation of the topic is different since the subject is familiar. Thus, the words tourism (117), medical (112), patients (95), hospital (93), and India (64) are in relevance to the main subject aim. The word count occurrence indicates relevance to the study topic, and that is why the word repetitions were the most.

The main motive here was to link the occurrence of words to the main aim or objectives of the study. This type of interpretation helps in providing relevance to the study or research. Thus, this is another type of interpretation for wordlist in Hamlet II. The above instructions help in the successful assessment of word list in Hamlet II software. The next article instructs on other word-based analyses; KWIC and text profile.