114 lines
3.8 KiB
Python
114 lines
3.8 KiB
Python
import json
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import nltk
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from nltk import TweetTokenizer
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tt = TweetTokenizer()
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special_chars = "1234567890.=?\",”$%^;&’*(…):!><"
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special_chars_CSV = "1234567890.=?\",”;$%^&’*(…):@!>#@<"
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words_tweet_tokenizer = []
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words_per_tweet = {}
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special_words = []
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hashtags = []
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wordsForCSV = []
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# Handles the tweet json, separates words into needed categories, and extracts hashtags
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with open("tweets.json", "r", encoding="utf-8") as tweetJson:
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tweetJsonData = json.load(tweetJson)
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for tweet in tweetJsonData:
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tempAppend = []
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tempWords = tt.tokenize(tweet["text"])
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words_per_tweet[tweet["id"]] = tempWords
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for word in tempWords:
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if not any(c in special_chars for c in word) and len(word) > 1:
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words_tweet_tokenizer.append(word)
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if word[0] == '#':
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hashtags.append(word)
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else:
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if len(word) > 1:
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special_words.append(word)
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if not any(c in special_chars_CSV for c in word) and len(word) > 1:
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tempAppend.append(word)
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wordsForCSV.append(tempAppend)
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# Counts hashtags and outputs the top
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hashtag_dictionary = {x: 0 for x in hashtags}
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for hashtag in hashtags:
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hashtag_dictionary[hashtag] += 1
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hashtag_dictionary = dict(sorted(hashtag_dictionary.items(), key=lambda item: item[1], reverse=True))
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print("===================")
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print("Top 10 #hashtags:")
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print("===================")
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count = 0
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for hashtag in hashtag_dictionary:
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if count < 10:
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print(hashtag, " ", hashtag_dictionary[hashtag])
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count += 1
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# Analyzes each word
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tweet_rated_emotion = {}
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word_emotion_dict = {}
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with open("AFINN-111.txt", "r", encoding="utf-8") as AFINNdict:
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for line in AFINNdict:
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words = nltk.word_tokenize(line)
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nr = words[len(words) - 1]
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strn = ""
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for x in range(len(words) - 1):
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strn += words[x]
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word_emotion_dict[strn] = nr
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for id in words_per_tweet:
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total_rating = 0
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for word in words_per_tweet[id]:
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if word.lower() in word_emotion_dict:
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total_rating += int(word_emotion_dict[word.lower()])
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tweet_rated_emotion[id] = total_rating
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print("==========================")
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print("Top 10 Positive :D Tweets:")
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print("==========================")
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tweet_rated_emotion = dict(sorted(tweet_rated_emotion.items(), key=lambda item: item[1], reverse=True))
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count = 0
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for tweet in tweet_rated_emotion:
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if count < 10:
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print(tweet, " ", tweet_rated_emotion[tweet])
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count += 1
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print("===========================")
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print("Top 10 Negative >:D Tweets:")
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print("===========================")
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tweet_rated_emotion = dict(sorted(tweet_rated_emotion.items(), key=lambda item: item[1], reverse=False))
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count = 0
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for tweet in tweet_rated_emotion:
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if count < 10:
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print(tweet, " ", tweet_rated_emotion[tweet])
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count += 1
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print("=====================================")
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print("All Tweets Rated by Emotional Damage:")
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print("=====================================")
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print(tweet_rated_emotion)
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# Creates the CSV file
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studentID = 10
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startingPoint = studentID * int(200/7)
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endingPoint = startingPoint + 200
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graph_dict = {}
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maxm = 0
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for i in range(startingPoint, endingPoint):
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for x in wordsForCSV[i]:
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graph_dict[x] = []
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filter_words = ["RT", "of", "it", "not", "but", "for", "are", "in", "my", "to", "by", "doing", "so"]
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file = open("data.csv", "w", encoding="utf-8")
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for i in range(startingPoint, endingPoint):
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for word in wordsForCSV[i]:
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for x in wordsForCSV[i]:
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if x is not word and x not in graph_dict[word] and x not in filter_words:
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graph_dict[word].append(x)
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for x in graph_dict:
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for z in range(len(graph_dict[x])):
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file.write(x + ",")
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file.write(graph_dict[x][z])
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if z != len(graph_dict[x]) - 1:
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file.write(",")
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file.write("\n")
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file.write("\n")
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