From 3fb575deb8e3ad87f13b578364730d55e98ad974 Mon Sep 17 00:00:00 2001
From: "Stephen Howell (MSFT)"
 <38020233+stephen-howell@users.noreply.github.com>
Date: Wed, 19 May 2021 20:47:11 +0100
Subject: [PATCH] Create lesson1_task3.py

---
 NLP/1-Introduction/solutions/lesson1_task3.py | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 NLP/1-Introduction/solutions/lesson1_task3.py

diff --git a/NLP/1-Introduction/solutions/lesson1_task3.py b/NLP/1-Introduction/solutions/lesson1_task3.py
new file mode 100644
index 000000000..6001cac7e
--- /dev/null
+++ b/NLP/1-Introduction/solutions/lesson1_task3.py
@@ -0,0 +1,23 @@
+from textblob import TextBlob
+
+# The book file is supplied, but you can get it (and many other books) yourself from Project Gutenberg
+with open('pride.txt', encoding="utf8") as f:
+    file_contents = f.read()
+
+book_pride = TextBlob(file_contents)
+positive_sentiment_sentences =[]
+negative_sentiment_sentences =[]
+
+for sentence in book_pride.sentences:
+    if sentence.sentiment.polarity == 1:
+        positive_sentiment_sentences.append(sentence)
+    if sentence.sentiment.polarity == -1:
+        negative_sentiment_sentences.append(sentence)
+
+print("The " + str(len(positive_sentiment_sentences)) + " most positive sentences:")
+for sentence in positive_sentiment_sentences:
+    print("+ " + str(sentence.replace("\n", "").replace("      ", " ")))
+
+    print("The " + str(len(negative_sentiment_sentences)) + " most negative sentences:")
+for sentence in negative_sentiment_sentences:
+    print("- " + str(sentence.replace("\n", "").replace("      ", " ")))