How to Use

The Contextualizer software is made to be very straight-forward and easy to use. The software works with plain text (.txt) files as input, and will output a North American formatted CSV spreadsheet as your output. More details on use are provided below.

Upon opening the software, you will see several basic options. The first place to start is your “List of Words to Contextualize”. In this area, you will include the words/phrases that you would like to capture:

These can be full words, words that end with wildcards, or multi-word phrases.

After your word list has been set, you can choose the number of words to retain on either side of your word list. For example, if you set the left and right word windows to 3, then whenever the word “happy” is detected, Contextualizer will capture the 3 words immediately prior to the word “happy” as well as the 3 words immediately following the word “happy”.

Consider the example sentence:

I don’t think that he looked very happy today, but he said that he was in a good mood.

In this sentence, Contextualizer will find the word “happy” and provide output that includes “he looked very”, “happy”, and “today but he”. If you want to capture a larger (or smaller) number of words on either side of items in your word list, this can be set easily.

Finally, it is important to ensure that the “Encoding of Text Files” option is set to match your input texts. If you are unsure about the encoding of your input .txt files, you should probably start by trying “utf-8” as your encoding.

Once all of your options are set, click the “Start” button to select the location of your text files and to begin analysis. Simple, right? Right! Afterwards, you will receive your output in the form of a CSV spreadsheet that can be opened by virtually all spreadsheet software (e.g., Excel, OpenOffice, etc.).

If you want to re-analyze the context provided in your output file, you may consider using EZPZTXT to re-aggregated your contextualized data back into .txt files for analysis with other programs, like LIWC or MEH.