def crossrefVsTwitter(yearBounds = [None, None], minTweetAge = None, maxTweetAge = None):
tweetVsCrossrefList = []
minTweetAge = 60*60*24*0
maxTweetAge = 60*60*24*100
totalDocs = 0
totalTweets = 0
nullWeights = 0
nonNullWeights = 0
for doc in filter(
lambda doc:
(doc.publicationDatetime().year==2012 and
doc.publicationDatetime().month>=6 and doc.publicationDatetime().month<=8),
SimpleDoc.getall()
):
docsTweets = filter(lambda tweet:
(not minTweetAge or (tweet.timestamp-doc.publicationTimestamp) >= minTweetAge) and
(not maxTweetAge or (tweet.timestamp-doc.publicationTimestamp) <= maxTweetAge),
doc.tweets)
def userWeight(tweet):
user = tweet.user()
return None if user is None else user.weight()
userWeights = map(lambda tweet: userWeight(tweet), docsTweets)
nullWeights += sum((1 for weight in userWeights if weight is None))
nonNullWeights += sum((1 for weight in userWeights if not weight is None))
tweetVsCrossrefList.append([doc.numCrossrefs(), 0 if len(userWeights) == 0 else sum(filter(lambda weight: weight != None, userWeights))])
totalDocs += 1
totalTweets += len(docsTweets)
print totalDocs
print totalTweets
print float(nullWeights) / (nullWeights+nonNullWeights)
# tweetVsCrossrefList = sorted(tweetVsCrossrefList, key=lambda tc: tc[1], reverse=True)[:100]
x, y = zip(*tweetVsCrossrefList)
paperFigure(plt)
plt.scatter(x, y)
# plt.title("Korrelation zwischen Tweets und Zitationen (Papieren zwischen " + str(yearBounds[0]) + " und " + str(yearBounds[1]) + "; #Docs: " + str(totalDocs) + ")")
plt.ylabel("#Tweets")
plt.xlabel("#Crossrefs")
#plt.xlim((0,200))
#plt.ylim((0,30))
p = numpy.polyfit(x, y, 1)
xTrend = range(min(x), max(x)+1)
yTrend = map(lambda x: numpy.polyval(p, x), xTrend)
plt.plot(xTrend, yTrend, color='r')
# plt.figtext(0.80, 0.05, 'korrelationskoeffizient: ' + str(korrelationskoeffizient(x, y)))
print 'korrelationskoeffizient: ' + str(korrelationskoeffizient(x, y))
plt.tight_layout()
plt.show()
from main.util.common import Sentiment
from main.util.plotting import paperFigure
import matplotlib.pyplot as plt
sentiments = Sentiment.fromFile("sentiments_all.json")
x = []
classes = ["1", "2", "3", "4"]
for index, classif in zip(range(0, len(classes)), classes):
xn = []
for sentiment in filter(lambda s: s.classification==classif, sentiments):
doc = sentiment.doc()
publicationTimestamp = doc.publicationTimestamp
tweetTimestamp = sentiment.id_tweetTimestamp
classification = sentiment.classification
print int(tweetTimestamp)
xn.append((int(tweetTimestamp)-publicationTimestamp)/(60*60*24))
x.append(xn)
paperFigure(plt)
plt.hist(x, label=["negative", "neutral", "positive", "t+l"], bins = [ -10, 0, 5, 10, 30, 100, 300 ], normed=True)
plt.legend()
plt.show()