discretemath-labs/LabPSA_3/networking.py

import json
import matplotlib.pyplot as plt
import nltk
from nltk import TweetTokenizer

tt = TweetTokenizer()
special_chars = "1234567890#@.=?\",”$%^&’*(…[]):!><"

likes_per_id = {}
retweets_per_id = {}
filter_words = ["sure"]
month_and_noun = {'2020-10': 0, '2020-11': 0, '2020-12': 0,'2022-01': 0, '2022-02': 0, '2022-03': 0,'2022-11': 0}
words_tweet_tokenizer = []
words_set = []
nouns_counted = {}
word_count_not_case_sensitive = {}
proper_nouns_counted = {}
words_per_tweet = {}
special_words = []
word_count_dict = {}
hashtags = []

# Handles the tweet json, separates words into needed categories
with open("tweets.json", "r", encoding="utf-8") as tweetJson:
    tweetJsonData = json.load(tweetJson)
    for tweet in tweetJsonData:
        tempWords = tt.tokenize(tweet["text"])
        words_per_tweet[tweet["id"]] = tempWords
        for word in tempWords:
            if not any(c in special_chars for c in word) and len(word) > 1:
                words_tweet_tokenizer.append(word)
                if word not in word_count_not_case_sensitive:
                    word_count_not_case_sensitive[word] = 1
                else:
                    word_count_not_case_sensitive[word] += 1
                if word not in word_count_dict and word.lower() not in word_count_dict:
                    word_count_dict[word.lower()] = 1
                else:
                    word_count_dict[word.lower()] += 1
                if word[0] == '#':
                    hashtags.append(word)
                if word not in words_set:
                    words_set.append(word)
            else:
                if len(word) > 1:
                    special_words.append(word)

# Outputs counted words
print("============")
print("Top 10 words")
print("============")
word_count_dict = dict(sorted(word_count_dict.items(), key=lambda item: item[1], reverse=True))
x = 1
for i in word_count_dict:
    if x <= 10:
        print(i, " ", word_count_dict[i])
    x += 1

# This piece of shit code counts the number of a word per month
word = input("Write a word")
with open('tweets.json', 'r', encoding='utf-8') as tweet_json:
    tweet_data = json.load(tweet_json)
    for i in range(len(tweet_data)):
        temp_msg = nltk.word_tokenize(tweet_data[i]["text"])
        temp_date = nltk.word_tokenize(tweet_data[i]["created_at"])
        for x in temp_msg:
            if x == word or x.lower() == word:
                month_and_noun[temp_date[0][:7]] += 1
x = list(month_and_noun.keys())
y = list(month_and_noun.values())
plt.bar(x, y, color='maroon', width=0.7)
plt.show()

# Counts nouns
for i in word_count_dict:
    ans = nltk.pos_tag([i])[0][1]
    if ans == 'NN' or ans == 'NNS' or ans == 'NNPS' or ans == 'NNP':
        if i not in filter_words and not any(c in ["\'"] for c in i):
            nouns_counted[i] = word_count_dict[i]
nouns_counted = dict(sorted(nouns_counted.items(), key=lambda item: item[1], reverse=True))

# Outputs counted nouns
print("============")
print("Top 10 nouns")
print("============")
x = 1
for i in nouns_counted:
    if x <= 10:
        print(i, " ", nouns_counted[i])
    x += 1

# Counts proper nouns
for i in word_count_not_case_sensitive:
    ans = nltk.pos_tag([i])[0][1]
    if ans == 'NN' or ans == 'NNS' or ans == 'NNPS' or ans == 'NNP':
        if i not in filter_words and not any(c in ["\'"] for c in i) and i[0].isupper():
            proper_nouns_counted[i] = word_count_not_case_sensitive[i]
proper_nouns_counted = dict(sorted(proper_nouns_counted.items(), key=lambda item: item[1], reverse=True))

# Outputs proper nouns
print("===================")
print("Top 10 proper nouns")
print("===================")
x = 1
for i in proper_nouns_counted:
    if x <= 10:
        print(i, " ", proper_nouns_counted[i])
    x += 1


# Counts likes and retweets for each tweet
with open('tweets.json', 'r', encoding='utf-8') as tweet_json:
    tweet_data = json.load(tweet_json)
    for tweet in tweet_data:
        likes_per_id[tweet["id"]] = tweet["likes"]
        retweets_per_id[tweet["id"]] = tweet["retweets"]

# Counts popularity by formula
popularity_nouns = {}
for noun in nouns_counted:
    normLikes = 0
    normRetweets = 0
    for id in words_per_tweet:
        if noun in words_per_tweet[id]:
            normLikes += likes_per_id[id]
            normRetweets += retweets_per_id[id]
    popularity_nouns[noun] = word_count_dict[noun] * (1.4 * normRetweets) * (1.2 * normLikes)
    if (noun == "right"):
        print("KID NOUN")
        print("KID RT")
        print(normRetweets)
        print("KID LIKES")
        print(normLikes)
        print("KID FREQ")
        print(word_count_dict[noun])
popularity_nouns = dict(sorted(popularity_nouns.items(), key=lambda item: item[1], reverse=True))

# Outputs popularity nouns
print("==========================")
print("Top 10 nouns by popularity")
print("==========================")
x = 1
for i in popularity_nouns:
    if x <= 10:
        print(i, " ", popularity_nouns[i])
    x += 1

# Suggestion stuff
word_sliced_count = {}
word = input("Write word for suggestion")
for x in word_count_dict:
    if x not in word_sliced_count:
        word_sliced_count[x] = 0
for x in word_count_dict:
    if word == x[:len(word)] and word != x:
        word_sliced_count[x] += word_count_dict[x]
word_sliced_count = dict(sorted(word_sliced_count.items(), key=lambda item: item[1], reverse=True))
print("===============")
print("Top suggestions")
print("===============")
x = 1
for i in word_sliced_count:
    if x <= 10:
        print(i, " ", word_sliced_count[i])
    x += 1

# Suggestion occurrences stuff
word = input("Write word for suggestion")
words_suggestion_counted = {}
for id in words_per_tweet:
    for i in range(len(words_per_tweet[id]) - 2):
        if words_per_tweet[id][i] == word:
            if words_per_tweet[id][i+1] not in words_suggestion_counted and len(words_per_tweet[id][i+1]) > 1:
                words_suggestion_counted[words_per_tweet[id][i+1]] = 1
            elif len(words_per_tweet[id][i+1]) > 1:
                words_suggestion_counted[words_per_tweet[id][i + 1]] += 1
words_suggestion_counted = dict(sorted(words_suggestion_counted.items(), key=lambda item: item[1], reverse=True))
print("==========================")
print("Top suggestion occurrences")
print("==========================")
x = 1
for i in words_suggestion_counted:
    if x <= 5:
        print(i, " ", words_suggestion_counted[i])
    x += 1