Textbook Analysis via Natural Language Processing
Code for the paper "Content Analysis of Textbooks via Natural Language Processing".
Lucy*, L., Demszky*, D., Bromley, P. & Jurafsky, D. (2019). Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas US HistoryTextbooks. Under Review. *indicates equal contribution
These scripts are for those who would like to perform analyses on their own textbook data (or any other data), to better understand the representation of minorities and women in text. You do not need any technical background to run these analyses. See our paper for a more detailed description of each method.
First, download this repository by running the following in the Terminal and then enter the directory:
git clone https://github.com/ddemszky/textbook-analysis.git
cd textbook-analysis
Create and activate virtual environment:
python3 -m venv env
source env/bin/activate
Install all required packages:
pip install -r requirements.txt
python -m spacy download en_core_web_sm # install SpaCy models for English
Our scripts were written for the studies described in our paper. It's likely you might be researching other research questions about other groups of people. For example, if you were analyzing differences in the descriptors of politicians pre-1850 and post-1850, you could run the files in our toolkit using a list of people terms categorized based on that comparison. Email us if you want some advice for your specific use case.
For tutorials:
conda deactivate # deactivate the base (needed for new Macs)
pip install jupyter
python -m ipykernel install --user --name='env'
jupyter notebook
Data Format
First, prepare your data such that each textbook is in a separate text file (simple .txt), in the same directory. Perform any clean-ups that you think might be necessary (e.g. removing characters that you do not want, remove short lines, etc.). Try to ensure complete sentences are on the same line; in some cases, digitization may split sentences across lines, and removing paratext (e.g. table of contents, glossaries, index) can help the analysis focus on the main content.
Counting the Mentions of People
Pre-processing
In order to count the number of times people are mentioned, it's important to first run co-reference resolution on the data so that pronouns, for example, are substituted by the nouns that they refer to. Run the script below, replacing the input and output directories with your paths.
python run_coref.py --input_dir data/source_txts --output_dir data/coref_resolved_txts
Note that this script may take a while to run on large files. It took ~1hr on our 15 textbooks, using my local machine.
Counting the Mentions of Demographic Groups
To count the frequency of mentions for different groups of people (e.g. different genders), run the following:
python count_mentions.py --input_dir data/coref_resolved_txts --output_dir results/ --people_terms wordlists/people_terms.csv
We include a people_terms.csv file in wordlists, but you can replace it with your own file. The format of this file should be the following: it should have 3 columns separated by a comma, the first including a word / phrase referring to people (lowercase), the second should be the demographic group that the word / phrase belongs to, and the third is the type of demographic. If a word belongs to multiple demographic groups, then add that as a separate line. For example:
bridesmaid,woman,gender
african,black,race/ethnicity
latina,latinx,race/ethnicity
latina,woman,gender
Our script takes unmarked terms, such as farmer, and if they are modified by a marker such as black farmer, recategorizes the instance of farmer to the category black.
An output file people_mentions.csv will be generated in the output directory. The file will have the following columns:
source_text: The name of the text file (corresponding to a textbook, for example).demographic: The demographic category.count: The number of terms belonging to that demographic in the given source text.
The above script works for unigrams (single words), but not yet for bigrams or trigrams (phrases). If a word contains a '-' (e.g. Asian-American), it is considered a trigram. We are hoping to offer broader support for this in the future.
Counting the Mentions of Named People
To count the frequency of mentions for named people (e.g. Eleanor Roosevelt), you first need to run Named Entity Recognition (NER). The following script will run NER on your files and it will also combine last names with the most recent full name in the data. It will also output a new dataset in ner_dir where named entities have standardized Wikidata names, e.g. Franklin D. Roosevelt -> Franklin Delano Roosevelt.
python count_names.py --input_dir data/coref_resolved_txts --ner_dir data/ner_coref_txts --output_dir results/named_people
This script will generate separate files for each textbook in the specified output directory, with counts of each named individual. It will also generate a file full2wikiname.json which saves aliases already queried from Wikidata allows you to rerun the script faster.
If you would also like to obtain demographic information for the named individuals automatically, you can run the following script. This script builds on Wikidata, which has a lot of missing information (e.g. it usually doesn't specify race for white people), but it has high coverage of gender information, for example. The input should be the output of count_names.py. Names will be matched based on Wikidata aliases.
python get_wikidata_attributes.py --input_dir results/named_people --output_dir results/
The output file will be a .csv, where the first column is the named entity as found in the text and the rest of the columns correspond to Wikidata attributes, including gender, race/ethnicity, and occupation. If the person was not found in the database, or the category was not listed, the value will be None. Note that the same person may show up multiple times if multiple Wikidata names are matched with it.
Looking at How People Are Described
Verbs and Adjectives
One way to understand how people are described is to look at the verbs and adjectives that they co-occur with. For this, you first need to run dependency parsing. You can run the following script, where the format of the people_terms file should be the way it is described above. Our paper used Dozat et al. (2017)'s dependency parser. Since many of the other tools in this repo are based on SpaCy and it is time consuming to train a parser from scratch, the script we provide here uses SpaCy's dependency parser.
python get_descriptors.py --input_dir data/ner_coref_txts --output_dir results/ --people_terms wordlists/people_terms.csv
This script will output people_descriptors.csv in the output directory, with the following columns:
source_text: The name of the text file (corresponding to a textbook, for example).people_term: The specific people term.category: The demographic category, or 'named' for named people.word: The verb / adjective.POS: The part of speech tag for the word (ADJ or VERB).relation: The dependency parsing relation
Note that terms associated with multiple demographic categories would be listed multiple times. For example, "black woman" would be listed under both "black" and "women". The verbs include those that the person is performing (nsubj) and those that are performed on the person (dobj).
The current implementation also obtains verbs and adjectives for named individuals, based on the output of the most popular named people of count_names.py.
Log odds ratio
We can look at which words are significantly more associated with one group vs another group based on word counts (Monroe et al. 2009). The --group1 and --group2 arguments are two groups of labels (comma separated) that you want to compare. For example, the command below compares common nouns referring to women with all other categories we have for common nouns. If a descriptor falls under multiple labels (cases of intersectionality), it is included in the first group but not the second. For example, the script handles words referring to women that are marked by ethnicity by not including them in the second group.
python run_log_odds.py --input_file results/people_descriptors.csv --output_dir results/ --group1 women --group2 "men,other,other minority,white,black,hispanic/latinx"
The output file is log_odds.txt in the results folder.
Note that you need quotation marks if the input argument has a space in it.
Power, Agency and Sentiment
Using external lexicons to quantify the affective connotations of words associated with people.
We use two lexicons in our paper.
The first, the NRC valence, arousal and dominance (VAD) lexicon contains a more than 20,000 English words. Download the zip file on their website and unzip it. Scores for words can be found in NRC-VAD-Lexicon.txt in the unzipped folder. You will want to add a line to the top of the file that says "Word Valence Arousal Dominance", where each word is separated by a tab. This allows our reader to use the file.
The second, the Connotation Frames dataset and Power and Agency frames. Click on the Full Labelled English Connotation Frame Data Set link in the first link and the download the verbs link in the second. Once these downloads are unzipped, sentiment frames can be found in the file labeled full_frame_info.txt and agency and power frames are in the file agency_power.csv.
Place these three lexicons (full_frame_info.txt, NRC-VAD-Lexicon.txt, and agency_power.csv) in the wordlists folder.
python get_lexicon_averages.py --input_file results/people_descriptors.csv --output_dir results/
The script outputs a csv file in the output directory, called lexicon_output.csv, with the following columns:
demographic: The demographic category.dimension: Name of the dimension.score: Score for the particular dimension.confidence_interval: 95% confidence interval for the score.
Look at common verbs or adjectives associated with a category and their scores for some dimension:
python get_lexicon_averages.py --input_file results/people_descriptors.csv --output_dir results/ --inspect True --category black --score_type agency
(Note that for power, the printed score is positive if the verb is power_agent, negative if power_theme, and the provided function does not take in account in which direction it is usually associated with the category of people.)
Measure Association Between Words via Word Embeddings
Another way to measure association between words is to represent words as vectors and look at their distance in the vector space. For this, you first need to create vectors for the words in your text. You can do so by running the following script. Note that this will take a while, depending on your data size and number of runs you want to do.
python run_word2vec.py --input_dir data/final_txts --output_dir data/word2vec_models \
--num_runs 50 --dim 100 --bootstrap
The input_dir argument should be a directory that includes the txt files. The script will create separate model files for each training run in output_dir.
By default, the script runs 50 separate bootstrap training runs -- this means that we sample from the sentences with replacement each time we train the model. This method, as found by Antoniak and Mimno (2018), ensures that we can measure word associations robustly, and calculate significance values. You can decrease the number of runs. If you do not want to use bootstrapping, you can set the number of runs to 1 and remove the --bootstrap argument.
You can also change dimension size of the embeddings (by default, it's set to 100). If you decrease it, you might get slightly lower quality embeddings but they will take up less space. If you increase it, you might get embeddings that capture more subtle semantics, but they will take up more space.
To get the most closely associated word with a particular group, run the following script. Here, words is the comma-separated list of seed words that refer to that group or concept. The script will print out the top closest words and their mean cosine similarity across all model runs.
python word2vec_get_closest.py --words woman,women,she,her,hers --word2vec_dir data/word2vec_models
If you want to compare the similarity of words from various topics to two sets of terms (e.g. terms referring to men vs women), follow the following steps:
- Create a dictionary file of terms referring to different themes, such as the one in
wordlists/liwc_queries.json, in the following format. If you only have one category, that's fine too, but you still need to follow the same format.
{
"home": ["home", "domestic", "household", "chores", "family"],
"work": ["work", "labor", "workers", "economy", "trade", "business",
"jobs", "company", "industry", "pay", "working", "salary", "wage"],
"achievement": ["power", "authority", "achievement", "control", "took control",
"won", "powerful", "success", "better", "efforts", "plan", "tried", "leader"],
}
-
Create two lists of words, one for one group of interest (e.g. women) and the other for the other group of interest (e.g. men). You can see examples in
wordlist/woman_terms.txtandwordlist/man_terms.txt. -
Run the following script, substituting the file references with your own paths. Change the arguments for
--name1and--name2with your group names.
python word2vec_calculate_similarity.py --queries wordlists/liwc_queries.json \
--words1 wordlists/woman_terms.txt --words2 wordlists/man_terms.txt \
--name1 Women --name2 Men --word2vec_dir data/word2vec_models \
--output_file results/word2vec_cosines.csv
The script will save a dataframe in the file specified by --output_file, with the following columns:
<name1>: Mean cosine similarity of query word to words in group 1.<name2>: Mean cosine similarity of query word to words in group 2.query: Query word.word category: Category that word belongs to.p value: p value for cosine similarity, calculated using two-tailed t test.
Analyzing Topics
To induce topics in your data, you first need to run a topic model. Our script runs LDA, using the very efficient MALLET package. In order to run this, you first need to download MALLET. Download and unzip the most recent MALLET package HERE.
The following script runs the topic model:
python get_topics.py \
--mallet_dir /Users/<YOUR_USERNAME>/mallet-2.0.8/bin \
--num_topics 70 \
--input_dir data/coref_resolved_txts \
--output_dir topics/topics_70 \
--stem
where the arguments are the following:
mallet_dir: Directory of the MALLET binary file.num_topics: Number of topics to induce.input_dir: Directory of textbook files.output_dir: Output directory for the topic model.stem: Whether to stem words before running the topic model (in the paper, we do).
The script will save the model and all associated files (e.g. vocabulary) in output_dir, and it will also create separate files for each book. You can inspect the topics in output_dir/topic_names.json, which contains the top 10 highest probability terms for each topic.
Note that this script runs the topic model on all books at once in input_dir, so if you want to get separate topic models for each book, then you should only include the relevant books in input_dir. If you want to run a topic model on all books, and then separate the topic distributions per book afterwards (this is what we did), you can do that with the script below.
Topic Prominence
You can obtain the number of sentences where each topic is prominent (above a ratio of .1, as determined by the topic model) using the following script.
python get_topic_prominence.py \
--topic_dir topics \
--textbook_dir data/coref_resolved_txts
where the arguments are the following:
topic_dir: Directory containing the topic files.textbook_dir: Directory containing the textbook files (for the purposes of obtaining titles).
The script will generate a dataframe, with the following columns:
book: Title of the book.topic_id: ID of the topic, as intopics/topic_names.json.topic_words: Top words associated with the topic, as intopics/topic_names.json.raw_count: Raw number of sentences where the topic is prominent for the given book.topic_proportion: The proportion of sentences where the topic is prominent for the given book.