Extracting Descriptive Statistics#
DaCy allows you to use other packages in the spaCy universe as you normally would - just powered by the DaCy models.
The following tutorial shows you how to use DaCy and TextDescriptives to extract a variety of metrics from text. For more information on the metrics that can be extracted, see the TextDescriptives documentation.
Data#
In this tutorial we’ll use TextDescriptives and DaCy, to get a quick overview of the SMS Spam Collection Data Set. The dataset contains 5572 SMS messages categorized as ham or spam.
Note
The estute among you will have noticed that this dataset is not Danish. This tutorial simply want to show how to use DaCy and TextDescriptives together and hopefully inspire you to use the tools on your own (Danish) data.
To start, let’s load a dataset and get a bit familiar with it.
from textdescriptives.utils import load_sms_data
df = load_sms_data()
df.head()
| label | message | |
|---|---|---|
| 0 | ham | Go until jurong point, crazy.. Available only ... |
| 1 | ham | Ok lar... Joking wif u oni... |
| 2 | spam | Free entry in 2 a wkly comp to win FA Cup fina... |
| 3 | ham | U dun say so early hor... U c already then say... |
| 4 | ham | Nah I don't think he goes to usf, he lives aro... |
df["label"].value_counts()
label
ham 4825
spam 747
Name: count, dtype: int64
Adding TextDescriptives components to DaCy#
Adding TextDescriptives components to a DaCy pipeline, follows exactly the same procedure as for any spaCy model. Let’s add the readability and dependency_distance components. Readability is a component that calculates readability metrics, and dependency_distance is a component that calculates the average dependency distance between words in a sentence. This can be seen a measure of sentence complexity.
Because we are using a DaCy model, the dependency_distance component will use the dependency parser from DaCy for its calculations.
import dacy
nlp = dacy.load("small") # load the latest version of the small model
nlp.add_pipe("textdescriptives/readability")
nlp.add_pipe("textdescriptives/dependency_distance")
From now on, whenever we pass a document through the pipeline (nlp), TextDescriptives will add readability and dependency distance metrics to the document.
Let’s load the data and pass it through the pipeline.
# to speed things up (especially on cpu) let's subsample the data
df = df.sample(500)
doc = nlp.pipe(df["message"])
import textdescriptives as td
# extract the metrics as a dataframe
metrics = td.extract_df(doc, include_text=False)
# join the metrics to the original dataframe
df = df.join(metrics, how="left")
df.head()
| label | message | dependency_distance_mean | dependency_distance_std | prop_adjacent_dependency_relation_mean | prop_adjacent_dependency_relation_std | flesch_reading_ease | flesch_kincaid_grade | smog | gunning_fog | ... | sentence_length_median | sentence_length_std | syllables_per_token_mean | syllables_per_token_median | syllables_per_token_std | n_tokens | n_unique_tokens | proportion_unique_tokens | n_characters | n_sentences | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1625 | ham | Hi Dear Call me its urgnt. I don't know whats ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3913 | ham | Today is ACCEPT DAY..U Accept me as? Brother S... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1530 | ham | I think chennai well settled? | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3356 | ham | Brainless Baby Doll..:-D;-), vehicle sariyag d... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4390 | ham | The greatest test of courage on earth is to be... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 28 columns
That’s it! Let’s do a bit of exploratory data analysis to get to know the data a bit more.
Exploratory Data Analysis#
With the metrics extracted, let’s do some quick exploratory data analysis to get a sense of the data. Let us start of by taking a look at the distribution of the readability metrics, lix.
import seaborn as sns
sns.boxplot(x="label", y="lix", data=df)
<Axes: xlabel='label', ylabel='lix'>
Let’s run a quick test to see if any of our metrics correlate strongly with the label
# encode the label as a boolean
df["is_ham"] = df["label"] == "ham"
# compute the correlation between all metrics and the label
metrics_correlations = metrics.corrwith(df["is_ham"]).sort_values(
key=abs, ascending=False
)
metrics_correlations[:10]
syllables_per_token_median -0.142166
proportion_unique_tokens -0.133919
coleman_liau_index -0.107163
token_length_mean -0.095158
n_unique_tokens -0.087053
token_length_std -0.082625
token_length_median -0.081647
dependency_distance_std 0.074962
prop_adjacent_dependency_relation_mean -0.060996
dependency_distance_mean 0.053602
dtype: float64
That’s some pretty high correlations! Notably we see that the mean dependency distance is correlated with ham. This makes sense, as the dependency distance is a measure of sentence complexity, and spam messages tend to be shorter and simpler.
Let’s try to plot it:
sns.kdeplot(df, x="dependency_distance_mean", hue="label", fill=True)
<Axes: xlabel='dependency_distance_mean', ylabel='Density'>
We can do a similar thing for the lix score, where we see that here isn’t a big difference between the two classes:
sns.kdeplot(df, x="lix", hue="label", fill=True)
<Axes: xlabel='lix', ylabel='Density'>
Cool! We’ve now done a quick analysis of the SMS dataset and found some differences in the distributions of some readability and dependency-distance metrics between the actual SMS’s and spam.
Next steps could be continue the exploratory data analysis or to build a simple classifier using the extracted metrics.