Track top used verbs in journal entries

To track the top used verbs in journal entries, you can analyze a set of journal texts and identify the most frequent verbs. Here’s a step-by-step approach to accomplish this:

Step 1: Data Collection

Gather a collection of journal entries. These can be your own, or you could use a sample of publicly available entries (if you’re working with a dataset for analysis). Ensure that the data set is diverse enough to represent various writing styles and contexts.

Step 2: Text Cleaning

Before analyzing the text, clean the data by:

• Removing punctuation marks, extra spaces, or irrelevant text like headings or signatures.

• Converting the text to lowercase to avoid duplication of words due to capitalization.

• Optionally, you can remove stopwords (commonly used words like “the,” “is,” etc.) if you’re only interested in meaningful verbs.

Step 3: Part-of-Speech Tagging

Using Natural Language Processing (NLP) techniques, perform Part-of-Speech (POS) tagging to identify the verbs in the journal entries. POS tagging involves assigning labels to words based on their role in a sentence (verb, noun, adjective, etc.). Tools like spaCy or NLTK in Python can do this effectively.

Step 4: Verb Frequency Calculation

Once you have identified all the verbs, count their occurrences. If you’re using a programming language like Python, you can store the verbs in a list or dictionary and tally the counts. Tools like Counter from the collections module in Python can be useful for this.

Step 5: Sort and Analyze

Sort the verbs by frequency to identify the top used ones. You can then analyze:

• Which verbs are most common in the journal entries.

• Which types of actions or emotions these verbs convey (e.g., verbs related to thinking, feeling, or doing).

Step 6: Visualization (Optional)

If you want to present the data visually, you can create charts or word clouds that show the top verbs. A bar chart or word cloud would make it easier to identify trends at a glance.

Example:

If you were analyzing the following journal entry:

“I walked to the park today. I felt so calm. I saw some birds flying above me. I enjoyed the peaceful atmosphere.”

The verbs could be:

• Walked

• Felt

• Saw

• Flying

• Enjoyed

After counting the occurrences, you could determine which verbs are most common in a larger set of journal entries.

Tools and Libraries:

• spaCy (for NLP and POS tagging)

• NLTK (for tokenization and POS tagging)

• Python’s Counter (for counting frequency)

• Matplotlib/Seaborn (for visualization)

• WordCloud library (for generating word clouds)

Would you like help with writing the code or setting up an analysis tool for this?