lemo-vocabulate 1.0.1

Dictionary-based text analysis tool for emotion and sentiment analysis. Python port of Vocabulate (Vine et al., 2020).

Vocabulate Python Edition

Vocabulate is a dictionary-based text analysis tool originally developed in C# for Windows.
This Python package allows you to tokenize, clean, and analyze texts based on custom dictionaries across any operating system (Windows, macOS, Linux).

DISCLAIMER: I do not take credit for this software. I simply reconfigured it to run using a higher-level programming language (i.e., python instead of C#). All credit goes to the original authors: Vine et al. (2020)

@article{vine2020natural,
  title={Natural emotion vocabularies as windows on distress and well-being},
  author={Vine, Vera, Boyd, Ryan L. and Pennebaker, James W.},
  journal={Nature Communications},
  volume={11},
  number={1},
  pages={4525},
  year={2020},
  doi={10.1038/s41467-020-18349-0}
}

Why This Package?

While the original Vocabulate software is powerful, this Python implementation offers several advantages:

• Cross-platform compatibility: Works on Windows, macOS, and Linux (original is Windows-only)
• Flexible input formats: Analyze text from pandas DataFrames, CSV files, single text files, or folders of text files
• Modern Python ecosystem: Integrates seamlessly with pandas, Jupyter notebooks, and other data science tools

---

Installation

Option 1: Install from PyPI (Recommended)

The simplest way to install LEMO Vocabulate is via pip:

pip install lemo-vocabulate

That's it! The package includes the AEV dictionary and stopwords file, so you can start analyzing text immediately.

Option 2: Install in a Conda Environment

For better dependency management, we'd recommend using a conda environment:

# Create and activate a new environment
conda create -n lemo python=3.8 -y # 

# Install the package
pip install lemo-vocabulate

Option 3: Install from Source

If you want to modify the code or contribute to development:

# Clone the repository
git clone https://github.com/Bushel-of-Lemons/LEMO_Vocabulate.git
cd LEMO_Vocabulate

# Install in editable mode
pip install -e .

Note: This package requires Python >= 3.8 and assumes you have python and pip already installed. Please refer to the official Python installation guide if needed.

---

Quick Start

import pandas as pd
from lemo_vocabulate import run_vocabulate_analysis, get_data_path

# Example using a DataFrame with included data files
df = pd.DataFrame({
    "user_id": ["user_1", "user_2"],
    "text": ["This is a sample text.", "Another example text."]
})

# Use the included dictionary and stopwords
results = run_vocabulate_analysis(
    dict_file=get_data_path("AEV_Dict.csv"),
    input_data=df,
    text_column="text",
    stopwords_file=get_data_path("stopwords.txt"),
    raw_counts=True
)

print(results.head())

Using Custom Files

You can still use your own dictionary and stopwords files, just provide the file paths:

# Use custom files
results = run_vocabulate_analysis(
    dict_file="path/to/your/custom_dict.csv",
    input_data=df,
    text_column="text",
    stopwords_file="path/to/your/custom_stopwords.txt",
    raw_counts=True
)

---

Features

• Tokenization designed for social media text

  • Twitter-aware tokenizer that handles:
    • Usernames (@user)
    • Hashtags (#happy)
    • Emojis and emoticons
    • URLs
    • Repeated characters (soooo good)
    • Punctuation-heavy social media text

• Stopword removal

  • Flexible stopword handling via file or string input

• Dictionary matching with multi-word wildcards

  • Compatible with custom dictionaries in CSV format
  • Example dictionary provided: Dictionary/AEV_Dict.csv

  Dictionary breakdown:

  Neg          94
  Pos          53
  AnxFear      20
  Anger        16
  Sadness      36
  NegUndiff    15
  Total words in dictionary: 162
  

• Comprehensive text metrics

  • Word count, type-token ratio, dictionary coverage
  • Category-level statistics (CWR, CCR, counts, unique counts)

• Flexible output

  • Returns Pandas DataFrame
  • Optional CSV export

---

Usage Examples

Analyzing a Pandas DataFrame

import pandas as pd
from lemo_vocabulate import run_vocabulate_analysis

# Create sample data
df = pd.DataFrame({
    "text_id": [1, 2, 3],
    "text": [
        "I am so agitated and aggravated!",
        "He was afraid of the dark.",
        "I am so happy happy happy! And sad."
    ]
})

# Run analysis
results = run_vocabulate_analysis(
    dict_file="Dictionary/AEV_Dict.csv",
    input_data=df,
    text_column="text",
    stopwords_file="stopwords.txt",
    raw_counts=True,
    output_csv="results.csv"
)

print(results.head())

Analyzing Text Files in a Folder or Single File

# Analyze a single text file
results = run_vocabulate_analysis(
    dict_file="Dictionary/AEV_Dict.csv",
    input_data="path/to/file.txt",
    stopwords_file="stopwords.txt",
    raw_counts=True
)

# Analyze all .txt files in a folder
results = run_vocabulate_analysis(
    dict_file="Dictionary/AEV_Dict.csv",
    input_data="path/to/folder",
    stopwords_file="stopwords.txt",
    raw_counts=False
)

Merging Results with Original Data

# Run analysis
df_results = run_vocabulate_analysis(
    dict_file="Dictionary/AEV_Dict.csv",
    input_data=df,
    text_column="text",
    stopwords_file="stopwords.txt",
    raw_counts=True
)

# Create text_id for merging
df_results['text_id'] = df_results.index

# Merge with original data
df_complete = df_results.drop(['text', 'Filename'], axis=1).merge(
    df,
    on='text_id',
    how='left'
)

# Reorder columns
cols = ['text_id', 'text'] + [col for col in df_complete.columns if col not in ['text_id', 'text']]
df_complete = df_complete[cols]

---

Stopwords

Stopword removal allows you to exclude very common function words (e.g., the, and, is, I, you) that typically do not carry psychological or semantic content. In Vocabulate, stopwords are removed after tokenization and before dictionary matching, which improves the interpretability of dictionary categories.

This package includes a pre-configured stopwords file that you can use immediately, or you can create your own custom stopwords file

Using a Stopwords File (Recommended)

Create a .txt file with one word per line:

the
and
is
i
you
to

Use it in your analysis:

from lemo_vocabulate import run_vocabulate_analysis, get_data_path
results = run_vocabulate_analysis(
    dict_file=get_data_path("AEV_Dict.csv"),
    input_data=df,
    text_column="text",
    stopwords_file=get_data_path("stopwords.txt")  # Use bundled stopwords
)

Using a Stopwords String

stopwords = "the\nand\nis\nbe\nnot\n"
results = run_vocabulate_analysis(
    dict_file="Dictionary/AEV_Dict.csv",
    input_data=df,
    text_column="text",
    stopwords_text=stopwords
)

How Stopwords Affect Output Metrics

Stopword removal does NOT affect:

• WC (whitespace word count)
• TC_Raw (raw token count)
• TTR_Raw (raw type-token ratio)

Stopword removal DOES affect:

Column	Effect
TC_Clean	Tokens after stopword removal
TTR_Clean	Based on clean tokens
TC_NonDict	Non-dictionary tokens after cleaning
DictPercent	Higher if stopwords filtered out
Category metrics	Only meaningful content words remain

---

Understanding the Output

The run_vocabulate_analysis function returns a Pandas DataFrame where each row corresponds to a single input text. Below is a detailed explanation of all output columns.

General Text Metrics

Column Name	Description
Filename	Name of the file or index of the row from the input DataFrame
text	The full original text that was analyzed
WC	Word count: total number of whitespace-separated tokens in the original text
TC_Raw	Token count after tokenizer processing (including punctuation, emoticons, etc.)
TTR_Raw	Type-Token Ratio for raw tokens: #unique tokens / TC_Raw * 100
TC_Clean	Token count after removing stopwords
TTR_Clean	Type-Token Ratio for cleaned tokens: #unique tokens / TC_Clean * 100
TC_NonDict	Number of tokens not matched to any dictionary concept
TTR_NonDict	Type-Token Ratio of non-dictionary tokens
DictPercent	Percent of tokens matched to dictionary concepts: num_matched_tokens / TC_Raw * 100
CapturedText	Concatenated string of all dictionary-matched words from the text

Category-Specific Metrics

For each category in the loaded dictionary (e.g., Neg, Pos, AnxFear, Anger, Sadness, NegUndiff), four metrics are provided:

Column Suffix	Description
_CWR	Category Word Ratio: percentage of unique words in the category relative to total words in text: unique_count / WC * 100
_CCR	Category Concept Ratio: percentage of unique words in the category relative to all matched tokens in that category: unique_count / total_count * 100
_Count	Raw Count: total number of occurrences of words from this category in the text (only if raw_counts=True)
_Unique	Unique Count: number of unique words in the text that matched this category (only if raw_counts=True)

Example: Category "Neg"

• Neg_CWR → % of total words in the text that were unique Neg words
• Neg_CCR → % of category words that were unique
• Neg_Count → Total Neg words matched
• Neg_Unique → Number of unique Neg words matched

---

Example Output

Filename	text	WC	TC_Raw	TTR_Raw	TC_Clean	TTR_Clean	TC_NonDict	TTR_NonDict	DictPercent	CapturedText	Neg_CWR	Neg_CCR	Neg_Count	Neg_Unique	Pos_CWR	Pos_CCR	Pos_Count	Pos_Unique	AnxFear_CWR	AnxFear_CCR	AnxFear_Count	AnxFear_Unique
0	I am so agitated and aggravated!	6	7	100.0	2	100.0	0	0.0	28.57	agitated aggravated	33.33	100.0	2	2	0.0	0.0	0	0	0.0	0.0	0	0
1	He was afraid of the dark....	6	8	100.0	3	100.0	2	100.0	12.5	afraid	16.67	100.0	1	1	0.0	0.0	0	0	16.67	100.0	1	1
2	Nothing to be afraid or agitated about. Yet I'm afraid, and it makes me want to agitate!!	17	21	85.71	6	83.33	2	100.0	19.05	afraid agitated afraid agitate	11.76	50.0	4	2	0.0	0.0	0	0	5.88	50.0	2	1
3	I am so happy happy happy! And sad.	8	10	80.0	4	50.0	0	0.0	40.0	happy happy happy sad	12.5	100.0	1	1	12.5	33.33	3	1	0.0	0.0	0	0
4	dislike disliked dislikes disliking/doo	5	6	100.0	5	100.0	1	100.0	66.67	dislike disliked dislikes disliking	20.0	25.0	4	1	0.0	0.0	0	0	0.0	0.0	0	0

---

Function Parameters

run_vocabulate_analysis(
    dict_file: str = None,           # Path to dictionary CSV file (required)
    input_data = None,               # DataFrame, file path, or folder path (required)
    text_column: str = None,         # Column name for text (required for DataFrame)
    stopwords_text: str = None,      # Stopwords as newline-separated string
    stopwords_file: str = None,      # Path to stopwords file
    raw_counts: bool = True,         # Include raw counts in output
    encoding: str = "utf-8",         # File encoding
    csv_delimiter: str = ",",        # CSV delimiter
    csv_quote: str = '"',            # CSV quote character
    output_csv: str = None,          # Optional output CSV path
    whitespace_method: str = 'new'   # 'new' (default, recommended 'legacy' (exact C# match)
)

Note about whitespace_method

This parameter controls how the WC (word count) metric is calculated and only affects this one column.

• 'new' (default, recommended): Uses standard Python whitespace tokenization that handles edge cases (multiple spaces, leading/trailing whitespace) consistently. Best for new analyses.

• 'legacy': Replicates the exact word counting behavior of the original C# Vocabulate software. Use this only if you need to exactly match results from the Windows version.

All other metrics (tokenization, dictionary matching, category ratios) are identical between both methods.

---

Citation

If you use this software in your research, please cite the original paper:

@article{vine2020natural,
  title={Natural emotion vocabularies as windows on distress and well-being},
  author={Vine, Vera, Boyd, Ryan L. and Pennebaker, James W.},
  journal={Nature Communications},
  volume={11},
  number={1},
  pages={4525},
  year={2020},
  doi={10.1038/s41467-020-18349-0}
}