Mean sentence sentiment

sentence_sentiment.py

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from textblob import TextBlob


def sentiment_polarity(string: str) -> float:
    polarity = TextBlob(string).sentiment[0]
    return polarity


# Load svo patterns and sentences
svo_data = pd.read_csv("svo_data.csv", index_col=0)
sen_data = pd.read_csv("sentence_data.csv", index_col=0)

# Apply sentiment(score) to the sentence column
sen_data["sentiment_polarity"] = sen_data.sentence.apply(sentiment_polarity)

# Print mean sentiment polarity score
mean_sentiment_scores = sen_data.groupby("keyterm").mean()

# Plot results
labels = [l.replace(" ", "\n") for l in mean_sentiment_scores.index.to_list()]
pos = np.arange(len(labels))
ax = mean_sentiment_scores.plot(
    kind="bar", color="lightblue", edgecolor="midnightblue", legend=False, rot=0
)
plt.axhline(0, 0, 1, linestyle="--", color="grey", lw=0.5)
ax.set_xticks(pos)
ax.set_xticklabels(labels)
plt.ylabel("Mean Sentiment Polarity")
plt.tight_layout()
plt.show()