def get_sentiment_inverse(user_id_1, user_id_2, common_hashtags=[]):
"""returns the sum of the inverses of the adoptions for each common hashtag
for which the users share the same opinion
"""
inverse = 0
if not common_hashtags:
common_hashtags = get_common_hashtags(user_id_1, user_id_2)
for hashtag in common_hashtags:
pol_1 = determine_polarity((_USER_TOPIC_DICT[user_id_1][hashtag][:3]))
pol_2 = determine_polarity((_USER_TOPIC_DICT[user_id_2][hashtag][:3]))
if pol_1 == pol_2:
inverse += 1 / len(_TOPIC_USER_DICT[hashtag])
return inverse
def get_sentiment_adamic_adar(user_id_1, user_id_2, common_hashtags=[]):
"""returns the sum of tha adamic adar distances for all common hashtags for
which the users share the same opinion
"""
adamic_adar_score = 0
if not common_hashtags:
common_hashtags = get_common_hashtags(user_id_1, user_id_2)
for hashtag in common_hashtags:
pol_1 = determine_polarity((_USER_TOPIC_DICT[user_id_1][hashtag][:3]))
pol_2 = determine_polarity((_USER_TOPIC_DICT[user_id_2][hashtag][:3]))
if pol_1 == pol_2:
adamic_adar_score += 1 / math.log(len(_TOPIC_USER_DICT[hashtag]))
return adamic_adar_score
def get_sentiment_disagreement(user_id_1, user_id_2, common_hashtags=[]):
"""returns the amount of common hashtags for which the two users expressed
opposite sentiment. Excluding hashtags for which the users expressed
objective opinion.
"""
disagreement_score = 0
if not common_hashtags:
common_hashtags = get_common_hashtags(user_id_1, user_id_2)
for hashtag in common_hashtags:
pol_1 = determine_polarity((_USER_TOPIC_DICT[user_id_1][hashtag][:3]))
pol_2 = determine_polarity((_USER_TOPIC_DICT[user_id_2][hashtag][:3]))
if pol_1 and pol_1 == -pol_2:
disagreement_score += 1
return disagreement_score
def get_sentiment_agreement(user_id_1, user_id_2,
exclude=[], common_hashtags=[]):
"""returns the amount of common hashtags for which the two user expressed
the same sentiment. The parameter exclude is a list of sentiments that
should not be taken into consideration when summing up the results.
For example if exclude equals [1, 0] then sentiment agreement will be
accumulated over topics affiliated only with negative sentiment (-1).
"""
agreement_score = 0
if not common_hashtags:
common_hashtags = get_common_hashtags(user_id_1, user_id_2)
for hashtag in common_hashtags:
pol_1 = determine_polarity((_USER_TOPIC_DICT[user_id_1][hashtag][:3]))
pol_2 = determine_polarity((_USER_TOPIC_DICT[user_id_2][hashtag][:3]))
if pol_1 == pol_2 and pol_1 not in exclude:
agreement_score += 1
return agreement_score
def get_sentiment_rarest(user_id_1, user_id_2, common_hashtags=[]):
"""returns the adoption of the rarest common hashtag excluding hashtags
for which the users expressed objective opinion. Returns -1 if no common
hashtag with polar agreement is found.
"""
adoption = maxint
if not common_hashtags:
common_hashtags = get_common_hashtags(user_id_1, user_id_2)
for hashtag in common_hashtags:
pol_1 = determine_polarity((_USER_TOPIC_DICT[user_id_1][hashtag][:3]))
pol_2 = determine_polarity((_USER_TOPIC_DICT[user_id_2][hashtag][:3]))
if pol_1 and pol_1 == pol_2:
adoption = min(adoption, len(_TOPIC_USER_DICT[hashtag]))
if adoption != maxint:
return adoption
else:
return -1
def get_size_of_common_hashtags(user_id_1, user_id_2, common_hashtags=[]):
"""returns the mean size of common hashtags by summing up the adoption of
all hashtags for which the users share the same opinion and diving that
by the sentiment agreement of the users
"""
mean_size = 0
if not common_hashtags:
common_hashtags = get_common_hashtags(user_id_1, user_id_2)
sentiment_agreement = get_sentiment_agreement(
user_id_1, user_id_2, common_hashtags=common_hashtags)
for hashtag in common_hashtags:
pol_1 = determine_polarity((_USER_TOPIC_DICT[user_id_1][hashtag][:3]))
pol_2 = determine_polarity((_USER_TOPIC_DICT[user_id_2][hashtag][:3]))
if pol_1 == pol_2:
mean_size += len(_TOPIC_USER_DICT[hashtag])
if sentiment_agreement:
return mean_size / sentiment_agreement
else:
return -1