Plot keyterms of a document

plot_keyterms.py

from typing import List, Tuple

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.axes import Axes
from textacy import extract, make_spacy_doc


def decompose_keyterms(keyterm_list: List[str]) -> Tuple:
    terms = [el[0].replace(" ", "\n") for el in keyterm_list]
    scores = np.asarray([el[1] for el in keyterm_list])
    return terms, scores


def make_barplot(
    scores: np.array,
    keyterms: List[str],
    ax: Axes = None,
    title: str = "barplot",
    ylabel: str = "ylabel",
    color: str = "lightblue",
    edgecolor: str = "midnightblue",
    align: str = "center",
    alpha: float=1.0,
) -> None:

    bars = ax.bar(
        np.arange(len(keyterms)), scores, align=align, color=color, alpha=alpha
    )
    for bar in bars:
        bar.set_edgecolor(edgecolor)

    ax.set_xticks(np.arange(len(keyterms)))
    ax.set_xticklabels(keyterms, fontsize=5)
    ax.set_ylabel(ylabel, fontsize=12)
    ax.set_title(title, fontsize=12)
    return ax


# Open data from .txt file
with open("news_article.txt", "r") as file:
    data = file.read().replace("\n", "")
article = data.replace(u"\xa0", u" ")

# Create doc object
doc = make_spacy_doc(article, lang="en_core_web_sm")

# KEYTERM EXTRACTION
# Each algorithm returns a list of tuples, containing the keyterm and a score
textrank = extract.keyterms.textrank(doc, normalize="lemma")
yake = extract.keyterms.yake(doc, normalize="lemma")
scake = extract.keyterms.scake(doc, normalize="lemma")
sgrank = extract.keyterms.sgrank(doc, normalize="lemma")

# Separate terms and relevancy scores
terms_textrank, scores_textrank = decompose_keyterms(textrank)
terms_yake, scores_yake = decompose_keyterms(yake)
terms_scake, scores_scake = decompose_keyterms(scake)
terms_sgrank, scores_sgrank = decompose_keyterms(sgrank)

# Make plot
fig, axes = plt.subplots(2, 2, figsize=(11, 8))
make_barplot(
    scores_textrank,
    terms_textrank,
    axes[0, 0],
    title="TextRank algorithm",
    ylabel="Importance",
)
make_barplot(
    scores_yake,
    terms_yake,
    axes[0, 1],
    title="YAKE algorithm",
    ylabel="Importance",
    color="lightcoral",
    edgecolor="firebrick",
)
make_barplot(
    scores_scake,
    terms_scake,
    axes[1, 0],
    title="sCAKE algorithm",
    ylabel="Importance",
    color="springgreen",
    edgecolor="darkgreen",
)
make_barplot(
    scores_sgrank,
    terms_sgrank,
    axes[1, 1],
    title="SGRank algorithm",
    ylabel="Importance",
    color="moccasin",
    edgecolor="darkorange",
)
plt.tight_layout()
plt.show()