"hate", "here", "watch", "air", "countries", "everyday", "words", "UK",
"ppl", "under", "bad", "jornada", "god", "home", "trending", "seeing",
"living", "crying", "nada", "trend", "everything", "saudara", "sin",
"pain", "horrible", "solo", "real", "Palestine", "remember", "Bashar",
"bombed", "día", "look", "Jesus", "eyes", "power", "strikes", "hear",
"mais", "chemical", "picture", "stand", "poor", "million", "vida",
"government", "situation", "tweet", "tears", "wrong", "hacer",
"triste", "watching", "porque", "cry", "word", "mean", "sleep",
"night", "enough", "amor", "victims", "money", "done", "man",
"affected", "untuk", "again", "mercy", "happen", "yet", "cruel",
"moment", "year", "poder", "hurts", "estamos", "humans", "parar",
"understand", "imagine"
] #":(", ":'("
# Calculates most used words in tweets:
fetchy.calculateMostUsed(700) #, smooth=12000)
# Recovers only wanted words:
fetchy.filterMostUsed(chosenWords)
# Saves data to JSON file:
fetchy.jsonMostUsed(20)
# Obtains a dictionary where words are keys and values are how many times they appear in the tweet texts:
#frequenciesDict = fetchy.getMostUsed();
# Initializes Word Cloud using the frequency dictionary:
#disp = Display(frequenciesDict, maskPath="Visualization/img/machine_gun.png")
# Creates an image:
#disp.produceImage("Visualization/syria.png")
