From a0eed8f6822b854e22079affac133fed8eba97d1 Mon Sep 17 00:00:00 2001
From: Daniel <59575049+lemoentjiez@users.noreply.github.com>
Date: Fri, 9 Dec 2022 11:34:04 +0200
Subject: [PATCH] Create network.py

---
 LabMD_3/network.py | 71 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 LabMD_3/network.py

diff --git a/LabMD_3/network.py b/LabMD_3/network.py
new file mode 100644
index 0000000..f03fa62
--- /dev/null
+++ b/LabMD_3/network.py
@@ -0,0 +1,71 @@
+import json
+import re
+import nltk
+from nltk import TweetTokenizer
+
+hashtags = []
+mapped_hashtags = dict()
+emotional_values = dict()
+tokenizer = TweetTokenizer()
+final_emotional_data = {}
+
+with open('AFINN-111.txt', encoding="utf-8") as file:
+    for line in file:
+        words = nltk.word_tokenize(line)
+        nr = words[len(words) - 1]
+        str = ""
+        for x in range(len(words) - 1):
+            str += words[x];
+        emotional_values[str] = nr
+
+with open('tweets.json', 'r', encoding='utf-8') as tweet_json:
+    tweet_data = json.load(tweet_json)
+    for i in range(len(tweet_data)):
+        emotion_rating = 0
+        words = tokenizer.tokenize(tweet_data[i]["text"])
+        for x in words:
+            if x[0] == '#' and len(x) > 1:
+                hashtags.append(x)
+            if re.sub("\s\s+", " ", x).lower() in emotional_values:
+                emotion_rating += int(emotional_values[x.lower()])
+        final_emotional_data[tweet_data[i]["id"]] = emotion_rating
+
+for i in range(len(hashtags)):
+    mapped_hashtags[hashtags[i]] = 0
+for i in range(len(hashtags)):
+    mapped_hashtags[hashtags[i]] += 1
+
+sorted_dict = dict(sorted(mapped_hashtags.items(), key=lambda item: item[1], reverse=True))
+counter = 10
+x = 1
+print("========================")
+print("Top #10 Hashtags")
+print("========================")
+for i in sorted_dict:
+    if x <= counter:
+        print(x,'.', i, " ", sorted_dict[i])
+    x += 1
+x = 1
+sorted_emotion_reverse = dict(sorted(final_emotional_data.items(), key=lambda item: item[1], reverse=True))
+sorted_emotion = dict(sorted(final_emotional_data.items(), key=lambda item: item[1]))
+print("========================")
+print("Top #10 Positive Tweets")
+print("=========================")
+x = 1
+for i in sorted_emotion_reverse:
+    if x <= counter:
+        print(i, " ", sorted_emotion_reverse[i])
+    x += 1
+print("========================")
+print("Top #10 Negative Tweets")
+print("========================")
+x = 1
+for i in sorted_emotion:
+    if x <= counter:
+        print(i, " ", sorted_emotion[i])
+    x += 1
+print("========================")
+print("All Emotional Values per ID")
+print("=========================")
+for x in final_emotional_data:
+    print(x, final_emotional_data[x])