From 7b2aa494d3086e81e8a2a16d31927399d66d5428 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?S=CD=A1urendra=20S=CD=A1id=20-=20AR?=
 <105810226+AR-sslink@users.noreply.github.com>
Date: Thu, 14 Sep 2023 00:24:27 +0530
Subject: [PATCH 1/2] Create Machine Learning - Sentiment Analysis

## Machine learning

Sentiment Analysis: sentiment analysis model that can analyze and classify text data (e.g., tweets, product reviews) as positive, negative, or neutral sentiment.
---
 Machine Learning - Sentiment Analysis | 30 +++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100644 Machine Learning - Sentiment Analysis

diff --git a/Machine Learning - Sentiment Analysis b/Machine Learning - Sentiment Analysis
new file mode 100644
index 000000000000..91db4c9ed1de
--- /dev/null
+++ b/Machine Learning - Sentiment Analysis	
@@ -0,0 +1,30 @@
+import nltk
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+# Download VADER lexicon (if not already downloaded)
+nltk.download('vader_lexicon')
+
+# Initialize the sentiment analyzer
+sid = SentimentIntensityAnalyzer()
+
+def analyze_sentiment(text):
+    # Calculate sentiment scores
+    sentiment_scores = sid.polarity_scores(text)
+    
+    # Determine sentiment label based on the compound score
+    compound_score = sentiment_scores['compound']
+    if compound_score >= 0.05:
+        return "Positive"
+    elif compound_score <= -0.05:
+        return "Negative"
+    else:
+        return "Neutral"
+
+# Input text for analysis
+text = "I love this product! It's amazing."
+
+# Perform sentiment analysis
+sentiment = analyze_sentiment(text)
+
+# Display the sentiment result
+print(f"Sentiment: {sentiment}")

From 90785deb902da2702c9319372a1b8423e5b05ab7 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 13 Sep 2023 18:58:23 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 Machine Learning - Sentiment Analysis | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Machine Learning - Sentiment Analysis b/Machine Learning - Sentiment Analysis
index 91db4c9ed1de..7003857427a2 100644
--- a/Machine Learning - Sentiment Analysis	
+++ b/Machine Learning - Sentiment Analysis	
@@ -10,7 +10,7 @@ sid = SentimentIntensityAnalyzer()
 def analyze_sentiment(text):
     # Calculate sentiment scores
     sentiment_scores = sid.polarity_scores(text)
-    
+
     # Determine sentiment label based on the compound score
     compound_score = sentiment_scores['compound']
     if compound_score >= 0.05: