def __initialize_senti(self):
self.senti = PySentiStr()
self.senti.setSentiStrengthPath(
str(Path.cwd() / 'lib' / 'SentiStrengthCom.jar'))
self.senti.setSentiStrengthLanguageFolderPath(str(Path.cwd() / 'lang'))
# simple test to make sure senti works
test = self.senti.getSentiment(['You are beautiful'], 'dual')
assert type(test) is list
assert type(test[0]) is tuple
def main():
#==========================================================================#
# criando o objeto do sentistrength e setando os caminhos dos arquivos
# auxiliares
#==========================================================================#
obj_sentistrength = PySentiStr()
obj_sentistrength.setSentiStrengthPath(SENTISTRENGTH_JAR_PATH)
obj_sentistrength.setSentiStrengthLanguageFolderPath(
SENTISTRENGTH_DATA_PATH)
#===========================================================================#
# realizando a leitura do arquivo frases.txt e colocando as linhas
# na lista file_lines (file.readlines() retorna essa lista)
#===========================================================================#
with open('frases.txt', 'r') as file:
file_lines = file.readlines()
#===========================================================================#
# iterando sobre a lista file_lines e realizando a análise de sentimentos
# dos textos obtendo como resultados 3 scores (dual, trinary e scale)
# similares e proporcionais para um mesmo texto de entrada
#===========================================================================#
for line in file_lines:
text = line.strip() # para removermos o \n ao final da linha
result_scale = obj_sentistrength.getSentiment(text, score='scale')
result_dual = obj_sentistrength.getSentiment(text, score='dual')
result_trinary = obj_sentistrength.getSentiment(text, score='trinary')
print(
'text: {0}\nresult_scale: {1}\nresult_dual: {2}\nresult_trinary: {3}\n'
.format(text, str(result_scale), str(result_dual),
str(result_trinary)))
def sentiment_analysis(tweet_sample, aggregate=True, mode='trinary'):
senti = PySentiStr()
senti.setSentiStrengthPath(sentistrength_jar_full_path)
senti.setSentiStrengthLanguageFolderPath(sentistrength_lan_full_path_en)
sentiment_dict = {}
if type(tweet_sample) is not dict: return 'No matches'
else:
for topic in tweet_sample.keys():
# Scores: scale, dual, binary and trinary
sentiment = senti.getSentiment(tweet_sample[topic], score=mode)
if (aggregate == True):
sentisum = 0
summary = {}
for sent in sentiment:
sentisum += sent[
2] # The trinary score returns a tuple, unless the others
summary['value'] = sentisum
if sentisum > 0: summary['sentiment'] = 'positive'
else: summary['sentiment'] = 'negative'
sentiment = summary
sentiment_dict[topic] = sentiment
return sentiment_dict
def tweet_word_sentiment(data):
'''
input: whole corpus
output: 1 dicts for tweet_word_sentiment,
keys: tweet_id, values: dict (keys={"max","min","distance"})
max--highest sentiment score among all words
min--lowest sentiment score among all words
distance-- difference between highest score and lowest score
'''
feature_dict = {}
# try:
senti = PySentiStr()
senti.setSentiStrengthPath('./SentiStrength.jar')
senti.setSentiStrengthLanguageFolderPath('./SentiStrengthData/')
for tweet in data:
tokenized = tweet.tweet_words()
new_words = [word for word in tokenized if word.isalnum()]
if not new_words:
feature_dict[tweet.tweet_id] = {"max": 0, "min": 0, "distance": 0}
continue
result = senti.getSentiment(new_words)
max_, min_ = result[0], result[0]
for score in result:
max_ = max(max_, score)
min_ = min(min_, score)
#feature_dict[tweet.tweet_id]={"max":max_,"min":min_,"distance":max_-min_}
feature_dict[tweet.tweet_id] = [max_, min_, max_ - min_]
return feature_dict
def sentistr(x):
senti = PySentiStr()
senti.setSentiStrengthPath("SentiStrength.jar")
senti.setSentiStrengthLanguageFolderPath("SentStrength_Data")
result = senti.getSentiment(
x,
score='trinary') #positive rating, negative rating and neutral rating
return result
def get_sentistrength(df):
senti = PySentiStr()
senti.setSentiStrengthPath('~/softwares/SentiStrengthCom.jar')
senti.setSentiStrengthLanguageFolderPath(
'~/softwares/SentStrength_Data_Sept2011/')
df["text"] = [t if t != "" else " " for t in df['text']]
result = senti.getSentiment(df["text"], score='trinary')
df["sentistrength_pos"] = [r[0] for r in result]
df["sentistrength_neg"] = [r[1] for r in result]
df["sentistrength_neutral"] = [r[2] for r in result]
return df
def main():
#mudar entrada
with open(
'./Comentarios_csv/Test/OPOVOOnline sobre escolha do novo reitor UFC.csv'
) as csv_file:
csv_dict_reader = csv.DictReader(csv_file)
senti = PySentiStr()
senti.setSentiStrengthPath(
"/home/caio/Documentos/Projeto Analise Comentarios Facebook/SentiStrength.jar"
)
senti.setSentiStrengthLanguageFolderPath(
"/home/caio/Documentos/Projeto Analise Comentarios Facebook/SentStrength_Data/portuguese/"
)
#mudar saída
with open('./Comentarios_csv/Test/teste.csv', 'w') as csvfile:
spamwriter = csv.writer(csvfile)
spamwriter.writerow(
["Comentário", "notaPositiva", "notaNegativa", "Sentimento"])
for row in csv_dict_reader:
#colocar nome da coluna que tem o comentario
if row["message"]:
sentence = row["message"]
#sentence = RemoveAccent(sentence)
sentence = Tokenize(sentence)
if sentence:
sentence = RemoveStopWords(sentence)
if sentence:
sentence = Stemming(sentence)
sentence = " ".join(sentence)
#sentistrength
result = senti.getSentiment(sentence,
score='binary')
if result[0][0] + result[0][1] <= -1:
sentiment = 'negativo'
elif result[0][0] + result[0][1] >= 1:
sentiment = 'positivo'
else:
sentiment = 'neutro'
spamwriter.writerow([
row["message"], result[0][0], result[0][1],
sentiment
])
print("finish!")
from utils import *
import pandas as pd
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
config = get_config('config.yaml')
from sentistrength import PySentiStr
senti = PySentiStr()
# Rocket HPC
senti.setSentiStrengthPath('/gpfs/space/home/enlik/GitRepo/master-thesis-2021/references/SentiStrengthCom.jar') # Note: Provide absolute path instead of relative path
senti.setSentiStrengthLanguageFolderPath('/gpfs/space/home/enlik/GitRepo/master-thesis-2021/references/SentiStrengthData/') # Note: Provide absolute path instead of relative path
df_freenow = pd.read_csv(config['csv_input_local']['freenow_apple_google_p1'], index_col=0)
df_freenow = df_freenow.reset_index(drop=True)
total_reviews = len(df_freenow)
print(f'Total English reviews: {total_reviews} \n')
df_freenow.review = df_freenow.review.astype(str)
# df_freenow = df_freenow.head(10) # testing purpose
listOfSentimentScores = []
for i in range(0, int(len(df_freenow))):
text_input = df_freenow.review[i]
star_rating = df_freenow.rating[i]
result = senti.getSentiment(text_input)
from sentistrength import PySentiStr
#inicializando sentistrength
senti = PySentiStr()
senti.setSentiStrengthPath("SentiStrength.jar")
senti.setSentiStrengthLanguageFolderPath("SentiStrength_Data")
frase1 = senti.getSentiment('The food here is GREAT!!', score='dual')
frase2 = senti.getSentiment('The food here is GREAT!!', score='binary')
frase3 = senti.getSentiment('The food here is GREAT!!', score='trinary')
frase4 = senti.getSentiment('The food here is GREAT!!', score='scale')
print("Frase1 na saída dual:", frase1)
print("Frase2 na saída binary:", frase2)
print("Frase3 na saída trinary:", frase3)
print("Frase4 na saída scale:", frase4)
# -Recommended to run sentistrength on the csv containing the column that contains full text
#The location of SentiStrength on your computer
SentiStrengthLocation = "C:/Users/ThinkPad/SpyderProjects/sentistrengthStuff/SentiStrength.jar"
#The location of the unzipped SentiStrength data files on your computer
SentiStrengthLanguageFolder = "C:/Users/ThinkPad/SpyderProjects/sentistrengthStuff/SentiStrength_Data/"
#Check if the paths are correct (if the paths are correct, you will see no flags thrown)
if not os.path.isfile(SentiStrengthLocation):
print("SentiStrength not found at: ", SentiStrengthLocation)
if not os.path.isdir(SentiStrengthLanguageFolder):
print("SentiStrength data folder not found at: ", SentiStrengthLanguageFolder)
# Initiate an object
senti = PySentiStr()
# set paths
senti.setSentiStrengthPath(SentiStrengthLocation)
senti.setSentiStrengthLanguageFolderPath(SentiStrengthLanguageFolder)
# Read csv (give your path)
all_files = glob.glob("C:/Users/ThinkPad/SpyderProjects/sentistrengthStuff/entropy files" + "/*.csv")
li = []
#Make a dataframe from appending lists
for filename in all_files:
df = pd.read_csv(filename, index_col=None, header=0, error_bad_lines=False)
li.append(df)
def prAnalysis(
config: Configuration,
senti: sentistrength.PySentiStr,
delta: relativedelta,
batchDates: List[datetime],
):
print("Querying PRs")
batches = prRequest(config.pat, config.repositoryOwner,
config.repositoryName, delta, batchDates)
batchParticipants = list()
batchComments = list()
for batchIdx, batch in enumerate(batches):
print(f"Analyzing PR batch #{batchIdx}")
# extract data from batch
prCount = len(batch)
participants = list(pr["participants"] for pr in batch
if len(pr["participants"]) > 0)
batchParticipants.append(participants)
allComments = list()
prPositiveComments = list()
prNegativeComments = list()
generallyNegative = list()
print(f" Sentiments per PR", end="")
semaphore = threading.Semaphore(15)
threads = []
for pr in batch:
comments = list(comment for comment in pr["comments"]
if comment and comment.strip())
# split comments that are longer than 20KB
splitComments = []
for comment in comments:
# calc number of chunks
byteChunks = math.ceil(sys.getsizeof(comment) / (20 * 1024))
if byteChunks > 1:
# calc desired max length of each chunk
chunkLength = math.floor(len(comment) / byteChunks)
# divide comment into chunks
chunks = [
comment[i * chunkLength:i * chunkLength + chunkLength]
for i in range(0, byteChunks)
]
# save chunks
splitComments.extend(chunks)
else:
# append comment as-is
splitComments.append(comment)
# re-assign comments after chunking
comments = splitComments
if len(comments) == 0:
prPositiveComments.append(0)
prNegativeComments.append(0)
continue
allComments.extend(comments)
thread = threading.Thread(
target=analyzeSentiments,
args=(
senti,
comments,
prPositiveComments,
prNegativeComments,
generallyNegative,
semaphore,
),
)
threads.append(thread)
for thread in threads:
thread.start()
for thread in threads:
thread.join()
print("")
# save comments
batchComments.append(allComments)
# get comment length stats
commentLengths = [len(c) for c in allComments]
generallyNegativeRatio = len(generallyNegative) / prCount
# get pr duration stats
durations = [(pr["closedAt"] - pr["createdAt"]).days for pr in batch]
print(" All sentiments")
commentSentiments = []
commentSentimentsPositive = 0
commentSentimentsNegative = 0
if len(allComments) > 0:
commentSentiments = senti.getSentiment(allComments)
commentSentimentsPositive = sum(
1 for _ in filter(lambda value: value >= 1, commentSentiments))
commentSentimentsNegative = sum(
1
for _ in filter(lambda value: value <= -1, commentSentiments))
toxicityPercentage = getToxicityPercentage(config, allComments)
centrality.buildGraphQlNetwork(batchIdx, participants, "PRs", config)
print(" Writing results")
with open(
os.path.join(config.resultsPath, f"results_{batchIdx}.csv"),
"a",
newline="",
) as f:
w = csv.writer(f, delimiter=",")
w.writerow(["NumberPRs", prCount])
w.writerow(["NumberPRComments", len(allComments)])
w.writerow(["PRCommentsPositive", commentSentimentsPositive])
w.writerow(["PRCommentsNegative", commentSentimentsNegative])
w.writerow(["PRCommentsNegativeRatio", generallyNegativeRatio])
w.writerow(["PRCommentsToxicityPercentage", toxicityPercentage])
with open(
os.path.join(config.metricsPath, f"PRCommits_{batchIdx}.csv"),
"a",
newline="",
) as f:
w = csv.writer(f, delimiter=",")
w.writerow(["PR Number", "Commit Count"])
for pr in batch:
w.writerow([pr["number"], pr["commitCount"]])
with open(
os.path.join(config.metricsPath,
f"PRParticipants_{batchIdx}.csv"),
"a",
newline="",
) as f:
w = csv.writer(f, delimiter=",")
w.writerow(["PR Number", "Developer Count"])
for pr in batch:
w.writerow([pr["number"], len(set(pr["participants"]))])
# output statistics
stats.outputStatistics(
batchIdx,
commentLengths,
"PRCommentsLength",
config.resultsPath,
)
stats.outputStatistics(
batchIdx,
durations,
"PRDuration",
config.resultsPath,
)
stats.outputStatistics(
batchIdx,
[len(pr["comments"]) for pr in batch],
"PRCommentsCount",
config.resultsPath,
)
stats.outputStatistics(
batchIdx,
[pr["commitCount"] for pr in batch],
"PRCommitsCount",
config.resultsPath,
)
stats.outputStatistics(
batchIdx,
commentSentiments,
"PRCommentSentiments",
config.resultsPath,
)
stats.outputStatistics(
batchIdx,
[len(set(pr["participants"])) for pr in batch],
"PRParticipantsCount",
config.resultsPath,
)
stats.outputStatistics(
batchIdx,
prPositiveComments,
"PRCountPositiveComments",
config.resultsPath,
)
stats.outputStatistics(
batchIdx,
prNegativeComments,
"PRCountNegativeComments",
config.resultsPath,
)
return batchParticipants, batchComments
def __aggregate(_posts, _media, _comments, session, logger):
_entries = list()
# Initialize sentistrength variable
senti = PySentiStr()
setup_sentistrength_path(senti)
for p in _posts:
try:
""" Id, name of a post """
entry = [p.id, p.name]
""" Number of version of a post """
version = __extract_version(p.discussion_url)
entry = entry + [version]
""" Number of tags for a product """
tags_number = session.query(func.count(
Topic.name)).filter(Topic.post_id == p.id).scalar()
entry = entry + [tags_number]
entry = entry + [p.featured, p.votes_count, p.day, p.created_at]
""" Time features """
launch_day = get_day_name_from_date(p.created_at.year,
p.created_at.month,
p.created_at.day)
best_launch_time = is_best_posted_time(p.created_at.hour,
p.created_at.minute,
p.created_at.second)
best_launch_day = is_best_launched_day(p.created_at.hour,
p.created_at.minute,
p.created_at.second,
launch_day)
max_follower = session.query(func.max(
User.followers_count)).scalar()
maker_id = session.query(
Apps.maker_id).filter(Apps.post_id == p.id).one()[0]
maker = session.query(
User.id, User.name, User.twitter_username, User.website_url,
User.followers_count).filter(User.id == maker_id).one()
weekend = is_weekend(maker.followers_count, max_follower,
launch_day)
entry = entry + [
launch_day, best_launch_time, best_launch_day, weekend
]
""" Presentation features """
entry = entry + [p.description]
if p.description:
""" Extraction of maker sentiment based on the description of his post """
maker_description_sentiment = __extract_sentiment(
senti, p.description)
entry = entry + [
maker_description_sentiment[0][0],
maker_description_sentiment[0][1], '', '', ''
]
# Text length
entry = entry + [len(p.description)]
# Sentence length
sentence = get_sentence(p.description)
sentence_length_sum = 0
for i in range(0, len(sentence)):
sentence_length_sum = sentence_length_sum + len(
sentence[i])
try:
sentence_length_average = sentence_length_sum / len(
sentence)
except ZeroDivisionError:
sentence_length_average = 0.0
entry = entry + [round(sentence_length_average)]
# Bullet points / Explicit features
bullet_points_explicit_features = __extract_bullet_points_explicit_features(
sentence)
entry = entry + [bullet_points_explicit_features]
# Emoji in description
emoji_description = __extract_emoji(p.description)
entry = entry + [emoji_description]
else:
entry = entry + [1, -1, '', '', '', 0, 0, 'No', 'No']
entry = entry + [p.tagline]
if p.tagline:
# Tagline length
entry = entry + [len(p.tagline)]
# Emoji in tagline
emoji_tagline = __extract_emoji(p.tagline)
entry = entry + [emoji_tagline]
else:
entry = entry + [0, 'No']
# Video, Tweetable images, Gif and Gif's number for a post
video = []
tweetable_images = []
gif = []
index_media = 0
while index_media < len(_media):
# check if the current post_id is equal to the post_id of the current media
if p.id == _media[index_media][0]:
# check if the media type is 'video'
if _media[index_media][1] == 'video':
# append to the list the link of the video
video = video + [_media[index_media][2]]
# calculate the image size passing its width and its height
roughly, ratio = calculate_aspect_ratio(
_media[index_media][3], _media[index_media][4])
# check if the image is a tweetable image
if (roughly == 2) and (ratio == 1):
# append to the list the image url
tweetable_images = tweetable_images + [
_media[index_media][5]
]
# check if the image is a gif image passing its url
found = is_gif(_media[index_media][5])
if found:
# append to the list the image url
gif = gif + [_media[index_media][5]]
index_media = index_media + 1
if video:
entry = entry + ['Yes']
else:
entry = entry + ['No']
if tweetable_images:
entry = entry + ['Yes']
else:
entry = entry + ['No']
if gif:
entry = entry + [gif, len(gif)]
else:
entry = entry + ['', len(gif)]
# Offers, Promo/Discount Codes, Questions, Maker_inside, Hunter_inside in comment body for a post
offers = []
questions = []
promo_codes = []
# maker_follows_up_on_comments = 0
hunter_follows_up_on_comments = 0
maker_comments = []
others_comments = []
comm_in_thread = []
hunter_id = session.query(
Hunts.hunter_id).filter(Hunts.post_id == p.id).one()[0]
index_comment = 0
while index_comment < len(_comments):
# check if the current post_id is equal to the post_id of the current comment
if p.id == _comments[index_comment][3]:
# extract offers passing the comment body
offer = __extract_offers(_comments[index_comment][1])
if offer:
offers = offers + offer
# extract questions passing the comment body
question = __extract_questions(_comments[index_comment][1])
if question:
questions = questions + question
# extract promo_codes passing the comment body
promo_code = __extract_promo_codes(
_comments[index_comment][1])
if promo_code:
promo_codes = promo_codes + promo_code
# # check if the maker follows up on the current comment
# if _comments[index_comment][4] == maker_id:
# maker_follows_up_on_comments = 1
# put comments in comm list (comment_id, comment_body, created_at, user_id)
comm_in_thread.append([
_comments[index_comment][0],
_comments[index_comment][1],
_comments[index_comment][2],
_comments[index_comment][4]
])
# check if the hunter follows up on the current comment
if _comments[index_comment][4] == hunter_id:
hunter_follows_up_on_comments = 1
""" Extraction of maker sentiment based on his post comments written the day of launch """
if _comments[index_comment][4] == maker_id:
# date of maker's comment written the day the post was launched
comment_date = _comments[index_comment][2]
# cut the comments written days after the post was launched
if (p.created_at.year == comment_date.year) and (
p.created_at.month == comment_date.month) and (
p.created_at.day == comment_date.day):
if not maker_comments:
maker_comments = [_comments[index_comment][1]]
else:
maker_comments = maker_comments + [
_comments[index_comment][1]
]
""" Extraction of others users sentiment based on their post comments written the day of launch """
if (_comments[index_comment][4] !=
maker_id) and (maker_id != hunter_id):
# date of others comment written the day the post was launched
comment_date = _comments[index_comment][2]
# cut the comments written days after the post was launched
if (p.created_at.year == comment_date.year) and (
p.created_at.month == comment_date.month) and (
p.created_at.day == comment_date.day):
if not others_comments:
others_comments = [_comments[index_comment][1]]
else:
others_comments = others_comments + [
_comments[index_comment][1]
]
index_comment = index_comment + 1
if offers:
entry = entry + ['Yes']
else:
entry = entry + ['No']
if promo_codes:
entry = entry + ['Yes']
else:
entry = entry + ['No']
if questions:
entry = entry + ['Yes']
else:
entry = entry + ['No']
# check if the maker writes the first comment in the thread
maker_started_comment_thread = 0
if comm_in_thread:
if (p.created_at.year == comm_in_thread[0][2].year) and (
p.created_at.month == comm_in_thread[0][2].month) and (
p.created_at.day == comm_in_thread[0][2].day):
if comm_in_thread[0][3] == maker_id:
maker_started_comment_thread = 1
# calculate maker comment ratio ((number of maker comments / number of all comments)*100)
number_maker_comments = 0
number_others_comments = 0
if comm_in_thread:
for i in range(0, len(comm_in_thread)):
if (p.created_at.year == comm_in_thread[i][2].year) and (
p.created_at.month == comm_in_thread[i][2].month
) and (p.created_at.day == comm_in_thread[i][2].day):
if comm_in_thread[i][3] == maker_id:
number_maker_comments = number_maker_comments + 1
else:
number_others_comments = number_others_comments + 1
thread_length = number_maker_comments + number_others_comments
try:
if maker_started_comment_thread == 1:
maker_comment_ratio = (number_maker_comments /
thread_length) * 100
else:
maker_comment_ratio = 0.0
except ZeroDivisionError:
maker_comment_ratio = 0.00
# Hunter reputation
hunter = session.query(
User.id, User.name, User.twitter_username, User.website_url,
User.followers_count,
User.apps_made_count).filter(User.id == hunter_id).one()
entry = entry + [
hunter.id, hunter.name, hunter.twitter_username,
hunter.website_url, hunter.followers_count,
hunter.apps_made_count, hunter_follows_up_on_comments
]
# Maker reputation
entry = entry + [
maker.id, maker.name, maker.twitter_username,
maker.website_url, maker.followers_count,
maker_started_comment_thread,
round(maker_comment_ratio, 2), thread_length
]
# check if the hunter is also the maker and append the variable hunter_is_maker to the list entry
hunter_is_maker = 0
if hunter_id == maker_id:
hunter_is_maker = 1
entry = entry + [hunter_is_maker]
# Append to the list the maker comment sentiment
if maker_comments:
comment = '\n'.join(maker_comments)
sentiment = __extract_sentiment(senti, comment)
else:
comment = ''
sentiment = [[1, -1]]
entry = entry + [
comment, sentiment[0][0], sentiment[0][1], '', '', ''
]
# Append to the list the others comment sentiment
if others_comments:
comment = '\n'.join(others_comments)
sentiment = __extract_sentiment(senti, comment)
else:
comment = ''
sentiment = [[1, -1]]
entry = entry + [
comment, sentiment[0][0], sentiment[0][1], '', '', ''
]
_entries.append(entry)
except NoResultFound as ex:
logger.error(str(ex))
continue
except MultipleResultsFound as ex:
logger.error(str(ex))
continue
return _entries
def main():
with open(
'./OPOVOOnline sobre escolha do novo reitor UFC.csv') as csv_file:
csv_dict_reader = csv.DictReader(csv_file)
senti = PySentiStr()
senti.setSentiStrengthPath(
"/home/caio/Documentos/Projeto Analise Comentarios Facebook/SentiStrength.jar"
)
senti.setSentiStrengthLanguageFolderPath(
"/home/caio/Documentos/Projeto Analise Comentarios Facebook/SentStrength_Data/portuguese/"
)
prev_message = ""
with open(
'/home/caio/Documentos/Projeto Analise Comentarios Facebook/Frases_Neutras.csv',
'w') as csvfile:
spamwriter = csv.writer(csvfile)
spamwriter.writerow(["Frase", "notaPositiva", "notaNegativa"])
#sentistrength
for row in csv_dict_reader:
if prev_message != row["message"] and row["message"]:
sentence = row["message"]
#sentence = RemoveAccent(sentence)
sentence = Tokenize(sentence)
if sentence:
sentence = RemoveStopWords(sentence)
if sentence:
sentence = Stemming(sentence)
sentence = " ".join(sentence)
result = senti.getSentiment(sentence,
score='binary')
if result[0][0] + result[0][1] == 0:
#salvar frase tokenizada
#spamwriter.writerow([sentence, result[0][0], result[0][1]])
#salvar frase inteira
spamwriter.writerow([
row["message"], result[0][0], result[0][1]
])
#publicacao com resposta de comentários
if row["object_link.connections.comments.message"] != 'null' and row[
"object_link.connections.comments.message"]:
sentence = row["object_link.connections.comments.message"]
#sentence = RemoveAccent(sentence)
sentence = Tokenize(sentence)
if sentence:
sentence = RemoveStopWords(sentence)
if sentence:
sentence = Stemming(sentence)
sentence = " ".join(sentence)
result = senti.getSentiment(sentence,
score='binary')
if result[0][0] + result[0][1] == 0:
#mostrar tokenizada
#spamwriter.writerow([sentence, result[0][0], result[0][1]])
#mostrar frase inteira
spamwriter.writerow([
row["object_link.connections.comments.message"],
result[0][0], result[0][1]
])
prev_message = row["message"]
print("finish!")
import sys
import json
import csv
import pandas as pd
import re
import demoji
import emoji
from datetime import datetime
from sentistrength import PySentiStr
from langdetect import detect
#prendo in input la caption. Deve essere stringa. Es: python3 txt_features.py "ciao come stai?"
caption = sys.argv[1]
senti = PySentiStr()
#impostare i 3 percorsi corretti, trovate i tre file nella cartella SentiStrength - 1) SentiStrength.jar - 2) SentStrength_Data_EN - 3) SentStrength_Data_IT2
senti.setSentiStrengthPath('./SentiStrength/SentiStrength.jar')
eng_path = './SentiStrength/SentStrength_Data_EN'
ita_path = './SentiStrength/SentStrength_Data_IT2'
def deEmojify(inputString):
return inputString.encode('ascii', 'ignore').decode('ascii')
#number of hashtag
def hashtag_count(string):
count = len([string for words in string.split() if words.startswith('#')])
return count
def pre_process_and_predict(sentence):
wordnet_lemmatizer = WordNetLemmatizer()
# # Replacing double quotes with single, within a string
sentence = sentence.replace("\"", "\'")
# # Removing unnecessary special characters, keeping only , ! ?
sentence = re.sub(r"[^!?,a-zA-Z0-9\ ]+", '', sentence)
# # Lemmatization on verbs
sentence = ' '.join([
wordnet_lemmatizer.lemmatize(word, pos='v')
for word in word_tokenize(sentence)
])
sn = SenticNet()
senti = PySentiStr()
senti.setSentiStrengthPath(CODE_PATH + '/sentistrength/SentiStrength.jar')
senti.setSentiStrengthLanguageFolderPath(
CODE_PATH + '/sentistrength/SentStrength_Data/')
sentiment_score = []
for sen in sent_tokenize(sentence):
senti_pos, senti_neg = senti.getSentiment(sen, score='dual')[0]
senti_pos -= 1
if senti_neg == -1:
senti_neg = 0
sum_pos_score = 0
sum_neg_score = 0
for word in word_tokenize(sen):
try:
w_score = float(sn.polarity_intense(word)) * 5
except KeyError:
w_score = 0
if w_score > 0:
sum_pos_score = sum_pos_score + w_score
elif w_score < 0:
sum_neg_score = sum_neg_score + w_score
sum_pos_score = (sum_pos_score + senti_pos) / 2
sum_neg_score = (sum_neg_score + senti_neg) / 2
sentiment_score.append((sum_pos_score, sum_neg_score))
additional_features_s = []
additional_features_ns = []
contra = []
pos_low = []
pos_medium = []
pos_high = []
neg_low = []
neg_medium = []
neg_high = []
for sum_pos_score, sum_neg_score in sentiment_score:
contra.append(int(sum_pos_score > 0 and abs(sum_neg_score) > 0))
pos_low.append(int(sum_pos_score < 0))
pos_medium.append(int(sum_pos_score >= 0 and sum_pos_score <= 1))
pos_high.append(int(sum_pos_score >= 2))
neg_low.append(int(sum_neg_score < 0))
neg_medium.append(int(sum_neg_score >= 0 and sum_neg_score <= 1))
neg_high.append(int(sum_neg_score >= 2))
additional_features_s = additional_features_s + [
max(pos_medium),
max(pos_high),
max(neg_medium),
max(neg_high)
]
additional_features_ns = additional_features_ns + [
max(pos_low), max(neg_low)
]
tweet = sentence
punctuation_count = SequencePunctuationCount(tweet)
character_count = SequenceCharacterCount(tweet)
capitalized_count = CapitalizedCount(tweet)
exclamation_count = ExclamationCount(tweet)
# emoji_count = EmojiCount(tweet)
f_count = [
punctuation_count, character_count, capitalized_count,
exclamation_count
]
for count in f_count:
f_low = int(count == 0)
f_medium = int(count >= 1 and count <= 3)
f_high = int(count >= 4)
additional_features_s = additional_features_s + [f_medium, f_high]
additional_features_ns = additional_features_ns + [f_low]
X = [sentence]
in_file = open(os.path.join(PICKLES_PATH, "vocab.pickle"), "rb")
vocab = pickle.load(in_file)
in_file.close()
in_file = open(os.path.join(PICKLES_PATH, "model.pickle"), "rb")
model = pickle.load(in_file)
in_file.close()
vectorizer = TfidfVectorizer(vocabulary=vocab)
X = vectorizer.fit_transform(X)
ans = int(sum(model.predict(X)))
print('Sentence : ', sentence)
print('Sarcastic features : ', additional_features_s)
print('Not Sarcastic features : ', additional_features_ns)
print('Contradict : ', max(contra))
print('Model Predict : ', ans)
print(
'My obs : ',
int((sum(additional_features_s) >= sum(additional_features_ns))
and max(contra) == 1))
print('Final Prd : ', end='')
if ans == 1 or ((sum(additional_features_s) >= sum(additional_features_ns))
and max(contra) == 1):
return True
else:
return False
import itertools
#from multiprocessing.pool import ThreadPool
#pool = ThreadPool(20) # However many you wish to run in parallel
from tqdm import tqdm
import glob
import os.path
import sys
from os import getcwd
from sentistrength import PySentiStr
senti = PySentiStr()
#senti.setSentiStrengthPath('C:\\SentiStrength\\SentiStrength.jar') # e.g. 'C:\Documents\SentiStrength.jar'
#senti.setSentiStrengthLanguageFolderPath('C:\\SentiStrength') # e.g. 'C:\Documents\SentiStrengthData\'
senti.setSentiStrengthPath(os.path.join(getcwd(),"SentiStrengthData/SentiStrength.jar"))
senti.setSentiStrengthLanguageFolderPath(os.path.join(getcwd(),"SentiStrengthData/"))
def preprocess_data(data):
data_out = pd.DataFrame()
data_out = data[['type','content']]
data_out.dropna(inplace=True)
return data_out
def count_words(text):
try:
return len(TextBlob(text).words)
#!/usr/bin/env python
# coding: utf-8
from utils import *
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
config = get_config('config.yaml')
from sentistrength import PySentiStr
senti = PySentiStr()
senti.setSentiStrengthPath(
'/Users/enlik/GitRepo/master-thesis-2021/references/SentiStrengthCom.jar'
) # Note: Provide absolute path instead of relative path
senti.setSentiStrengthLanguageFolderPath(
'/Users/enlik/GitRepo/master-thesis-2021/references/SentiStrengthData/'
) # Note: Provide absolute path instead of relative path
# # Sample use case of SentiStrength
result = senti.getSentiment('What a bad day')
print(result)
str_arr = ['What a lovely day', 'What a bad day']
result = senti.getSentiment(str_arr, score='scale')
print(result)
str_arr = ['What a lovely day', 'What a bad day']
result = senti.getSentiment(str_arr, score='dual')
print(result)
from sentistrength import PySentiStr
senti = PySentiStr()
senti.setSentiStrengthPath('data/sentistrength/SentiStrength5.jar')
senti.setSentiStrengthLanguageFolderPath('data/sentistrength/SentStrength_Data')
def analyse_sentence(sentence):
return senti.getSentiment(sentence)
class Maestro:
def __init__(self, df, output_path, output_name, batch):
# storing variables
self.df = df
self.filename = Path(output_path) / output_name
self.raw_file = '{}_raw.csv'.format(self.filename)
self.batch = batch
# initialize tools
self.translator = Translator()
self.__initialize_senti()
# collect jobs
job_list = self.__collect_jobs()
self.total_job = len(job_list)
# initialize queues
self.jobs = Queue(maxsize=self.total_job)
for job in job_list:
self.jobs.put(job)
self.results = Queue(maxsize=self.total_job)
# setup threading variables
self.stop = threading.Event()
self.worker_ct_lock = threading.Lock()
self.worker_ct = 0 # num_of_spawned worker
def __initialize_senti(self):
self.senti = PySentiStr()
self.senti.setSentiStrengthPath(
str(Path.cwd() / 'lib' / 'SentiStrengthCom.jar'))
self.senti.setSentiStrengthLanguageFolderPath(str(Path.cwd() / 'lang'))
# simple test to make sure senti works
test = self.senti.getSentiment(['You are beautiful'], 'dual')
assert type(test) is list
assert type(test[0]) is tuple
def __collect_jobs(self):
try:
out_df = pd.read_csv(self.raw_file, header=None)
processed_ser = self.df['tweetid'].isin(out_df[1])
except FileNotFoundError:
zeros = np.zeros((len(self.df.index), ), dtype=bool)
processed_ser = pd.Series(zeros)
job_list = processed_ser[~processed_ser].index
job_list = list(grouper(job_list, self.batch))
if len(job_list) > 0:
job_list[-1] = tuple(job for job in job_list[-1]
if job is not None)
return job_list
def __despawn_worker(self):
with self.worker_ct_lock:
self.worker_ct = self.worker_ct - 1
def __translate(self, thread_num):
with self.worker_ct_lock:
self.worker_ct = self.worker_ct + 1
while not self.stop.is_set() and not self.jobs.empty():
job = self.jobs.get()
try:
mini_df = self.df.loc[job, ] # trailing comma is needed
ids = mini_df.iloc[:, 0]
items = mini_df.iloc[:, -1].to_numpy().tolist()
except Exception as e:
print('Worker #{} got pandas error: {}'.format(thread_num, e))
break
try:
if len(items) == 1:
translations = [self.translator.translate(items)]
else:
translations = self.translator.translate(items)
except Exception as e:
print('Worker #{} got translation error: {}'.format(
thread_num, e))
break
self.results.put((job, ids, translations))
self.__despawn_worker()
def __save(self, results):
with open(self.raw_file, 'a', encoding='utf-8',
newline='') as csv_file:
writer = csv.writer(csv_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
writer.writerows(results)
def __process(self, score='dual'):
total_batch = int(np.ceil(len(self.df.index) / self.batch))
pbar = tqdm(total=total_batch, initial=(total_batch - self.total_job))
while not self.stop.is_set() or not self.results.empty():
time.sleep(2)
if not self.results.empty():
# merges all results
job_list, id_list, translation_list = ([], [], [])
steps = 0
while not self.results.empty():
job, ids, translations = self.results.get()
job_list.extend(job)
id_list.extend(ids)
translation_list.extend(translations)
steps = steps + 1
# analyze sentiments
texts = [tr.text for tr in translation_list]
try:
sentis = self.senti.getSentiment(texts, score)
except Exception as e:
print('Process got sentistrength error:', e)
break
try:
rows = [
(order, i, *senti, tr.src, text)
for order, i, senti, tr, text in zip(
job_list, id_list, sentis, translation_list, texts)
]
except Exception as e:
print(e)
break
try:
self.__save(rows)
except Exception as e:
print('Process got on save error:', e)
break
pbar.update(steps)
time.sleep(.1) # prevent too much loop checking
if not self.stop.is_set():
self.stop.set() # force stop all threads
print('Rebuilding...')
self.__rebuild()
print('Exiting...')
pbar.close()
def __rebuild(self):
try:
sf = pd.read_csv(self.raw_file,
header=None,
names=[
'order', 'tweetid', '+', '-', 'src_lang',
'translation'
])
sf.sort_values('order', inplace=True)
sf.to_csv('{}.csv'.format(self.filename), index=None)
except FileNotFoundError:
pass
except Exception as e:
print(ERR_STR.format('rebuild', 'on rebuilding csv'), e)
def play(self, n_thread=1):
if n_thread < 1:
return
with ThreadPoolExecutor(max_workers=n_thread + 1) as executor:
try:
executor.map(self.__translate, range(n_thread))
print('Spawing {} workers...'.format(n_thread))
while self.worker_ct is 0:
pass # waiting for any worker being spawned
print('Aye, Sir!')
executor.submit(self.__process)
# as long as there are atleast a worker
while self.worker_ct > 0:
# wait for any keyboard interrupt
time.sleep(.5) # power napping for half second
# either no job left or all worker has been despawned
self.stop.set()
if self.jobs.empty():
print('All done!')
if self.worker_ct is 0:
print('All workers quit their job!')
except KeyboardInterrupt:
print('\nKeyboard interrupt')
except Exception as e:
print(ERR_STR.format('play', 'something went wrong'), e)
finally:
self.stop.set()
print('Byee 👋')
import xml.etree.ElementTree as xml
from sentistrength import PySentiStr
#inicializando sentistrength
sstrength = PySentiStr()
sstrength.setSentiStrengthPath("SentiStrength.jar")
sstrength.setSentiStrengthLanguageFolderPath("SentiStrength_Data")
# Dada uma lista com as respostas, retorna uma lista com os valores de sentimento
# gerados pelo SentiStr
def analise_sentistr(respostas):
return sstrength.getSentiment(respostas)
import pandas as pd
from sentistrength import PySentiStr
senti = PySentiStr()
senti.setSentiStrengthPath(
'SentiStrengthCom.jar'
) # Note: Provide absolute path instead of relative path
senti.setSentiStrengthLanguageFolderPath(
'SentStrength_Data_Sept2011'
) # Note: Provide absolute path instead of relative path
str_arr = ['What a lovely day', 'What a bad day']
result = senti.getSentiment(str_arr)
print(result)
result = senti.getSentiment(str_arr, score='scale')
print(result)
# OR, if you want dual scoring (a score each for positive rating and negative rating)
result = senti.getSentiment(str_arr, score='dual')
print(result)
# OR, if you want binary scoring (1 for positive sentence, -1 for negative sentence)
result = senti.getSentiment(str_arr, score='binary')
print(result)
# OR, if you want trinary scoring (a score each for positive rating, negative rating and neutral rating)
result = senti.getSentiment(str_arr, score='trinary')
print(result)
def commitBatchAnalysis(
idx: int, senti: PySentiStr, commits: List[git.Commit], config: Configuration
):
authorInfoDict = {}
timezoneInfoDict = {}
experienceDays = 150
# traverse all commits
print("Analyzing commits")
startDate = None
if config.startDate is not None:
startDate = datetime.strptime(config.startDate, "%Y-%m-%d")
startDate = startDate.replace(tzinfo=pytz.UTC)
# sort commits
commits.sort(key=lambda o: o.committed_datetime, reverse=True)
commitMessages = []
commit: Commit
lastDate = None
firstDate = None
realCommitCount = 0
for commit in Bar("Processing").iter(commits):
if startDate is not None and startDate > commit.committed_datetime:
continue
if lastDate is None:
lastDate = commit.committed_date
firstDate = commit.committed_date
realCommitCount = realCommitCount + 1
# extract info
author = authorIdExtractor(commit.author)
timezone = commit.author_tz_offset
time = commit.authored_datetime
# get timezone
timezoneInfo = timezoneInfoDict.setdefault(
timezone, dict(commitCount=0, authors=set())
)
# save info
timezoneInfo["authors"].add(author)
if commit.message and commit.message.strip():
commitMessages.append(commit.message)
# increase commit count
timezoneInfo["commitCount"] += 1
# get author
authorInfo = authorInfoDict.setdefault(
author,
dict(
commitCount=0,
sponsoredCommitCount=0,
earliestCommitDate=time,
latestCommitDate=time,
sponsored=False,
activeDays=0,
experienced=False,
),
)
# increase commit count
authorInfo["commitCount"] += 1
# validate earliest commit
# by default GitPython orders commits from latest to earliest
if time < authorInfo["earliestCommitDate"]:
authorInfo["earliestCommitDate"] = time
# check if commit was between 9 and 5
if not commit.author_tz_offset == 0 and time.hour >= 9 and time.hour <= 17:
authorInfo["sponsoredCommitCount"] += 1
print("Analyzing commit message sentiment")
sentimentScores = []
commitMessageSentimentsPositive = []
commitMessageSentimentsNegative = []
if len(commitMessages) > 0:
sentimentScores = senti.getSentiment(commitMessages)
commitMessageSentimentsPositive = list(
result for result in filter(lambda value: value >= 1, sentimentScores)
)
commitMessageSentimentsNegative = list(
result for result in filter(lambda value: value <= -1, sentimentScores)
)
print("Analyzing authors")
sponsoredAuthorCount = 0
for login, author in authorInfoDict.items():
# check if sponsored
commitCount = int(author["commitCount"])
sponsoredCommitCount = int(author["sponsoredCommitCount"])
diff = sponsoredCommitCount / commitCount
if diff >= 0.95:
author["sponsored"] = True
sponsoredAuthorCount += 1
# calculate active days
earliestDate = author["earliestCommitDate"]
latestDate = author["latestCommitDate"]
activeDays = (latestDate - earliestDate).days + 1
author["activeDays"] = activeDays
# check if experienced
if activeDays >= experienceDays:
author["experienced"] = True
# calculate percentage sponsored authors
percentageSponsoredAuthors = sponsoredAuthorCount / len([*authorInfoDict])
# calculate active project days
firstCommitDate = None
lastCommitDate = None
if firstDate is not None:
firstCommitDate = datetime.fromtimestamp(firstDate)
if lastDate is not None:
lastCommitDate = datetime.fromtimestamp(lastDate)
daysActive = 0
if lastCommitDate is not None:
daysActive = (lastCommitDate - firstCommitDate).days
print("Outputting CSVs")
# output author days on project
with open(
os.path.join(config.metricsPath, f"authorDaysOnProject_{idx}.csv"),
"a",
newline="",
) as f:
w = csv.writer(f, delimiter=",")
w.writerow(["Author", "# of Days"])
for login, author in authorInfoDict.items():
w.writerow([login, author["activeDays"]])
# output commits per author
with open(
os.path.join(config.metricsPath, f"commitsPerAuthor_{idx}.csv"), "a", newline=""
) as f:
w = csv.writer(f, delimiter=",")
w.writerow(["Author", "Commit Count"])
for login, author in authorInfoDict.items():
w.writerow([login, author["commitCount"]])
# output timezones
with open(
os.path.join(config.metricsPath, f"timezones_{idx}.csv"), "a", newline=""
) as f:
w = csv.writer(f, delimiter=",")
w.writerow(["Timezone Offset", "Author Count", "Commit Count"])
for key, timezone in timezoneInfoDict.items():
w.writerow([key, len(timezone["authors"]), timezone["commitCount"]])
# output results
with open(
os.path.join(config.resultsPath, f"results_{idx}.csv"), "a", newline=""
) as f:
w = csv.writer(f, delimiter=",")
w.writerow(["CommitCount", realCommitCount])
w.writerow(["DaysActive", daysActive])
w.writerow(["FirstCommitDate", "{:%Y-%m-%d}".format(firstCommitDate)])
w.writerow(["LastCommitDate", "{:%Y-%m-%d}".format(lastCommitDate)])
w.writerow(["AuthorCount", len([*authorInfoDict])])
w.writerow(["SponsoredAuthorCount", sponsoredAuthorCount])
w.writerow(["PercentageSponsoredAuthors", percentageSponsoredAuthors])
w.writerow(["TimezoneCount", len([*timezoneInfoDict])])
outputStatistics(
idx,
[author["activeDays"] for login, author in authorInfoDict.items()],
"AuthorActiveDays",
config.resultsPath,
)
outputStatistics(
idx,
[author["commitCount"] for login, author in authorInfoDict.items()],
"AuthorCommitCount",
config.resultsPath,
)
outputStatistics(
idx,
[len(timezone["authors"]) for key, timezone in timezoneInfoDict.items()],
"TimezoneAuthorCount",
config.resultsPath,
)
outputStatistics(
idx,
[timezone["commitCount"] for key, timezone in timezoneInfoDict.items()],
"TimezoneCommitCount",
config.resultsPath,
)
outputStatistics(
idx,
sentimentScores,
"CommitMessageSentiment",
config.resultsPath,
)
outputStatistics(
idx,
commitMessageSentimentsPositive,
"CommitMessageSentimentsPositive",
config.resultsPath,
)
outputStatistics(
idx,
commitMessageSentimentsNegative,
"CommitMessageSentimentsNegative",
config.resultsPath,
)
return authorInfoDict, daysActive
import math
print(os.getcwd())
os.chdir("C:/Users/marcs/OneDrive/Bureaublad/Master/Thesis")
df = pd.DataFrame()
k=0
print("Start part 1:")
until = datetime.datetime(2019,1,1)
since = datetime.datetime(2018,12,31)
init_start = datetime.datetime.now()
afinn = Afinn(emoticons=True)
senti = PySentiStr()
senti.setSentiStrengthPath('C:/Users/marcs/OneDrive/Bureaublad/Master/Thesis/SentiStrength.jar') # Note: Provide absolute path instead of relative path
senti.setSentiStrengthLanguageFolderPath('C:/Users/marcs/OneDrive/Bureaublad/Master/Thesis/SentiStrength_Data/') # Note: Provide absolute path instead of relative path
for j in list(range(100000)):
start = datetime.datetime.now()
res = None
while res is None:
try:
tweetCriteria = got.manager.TweetCriteria().setQuerySearch('$HAS')\
.setSince(since.strftime('%Y-%m-%d'))\
.setUntil(until.strftime('%Y-%m-%d'))\
.setMaxTweets(10000)\
.setEmoji("unicode")\
.setLang("en")
tweet = got.manager.TweetManager.getTweets(tweetCriteria)
# remove stopwords
stop_words = set(stopwords.words('english'))
from nltk.tokenize import word_tokenize
tokens = word_tokenize(result)
result = [i for i in tokens if not i in stop_words]
# stemming
# stemmer= PorterStemmer()
# newResult = []
# for word in result:
# newResult.append(stemmer.stem(word))
# print(newResult)
return result
senti = PySentiStr()
senti.setSentiStrengthPath(
'C:\ProgramData\Anaconda3\Lib\site-packages\sentistrength\SentiStrength.jar'
)
senti.setSentiStrengthLanguageFolderPath(
'C:\ProgramData\Anaconda3\Lib\site-packages\sentistrength\\')
data = pd.read_csv("D:\senior\sentiment\Moodle_comments2.csv")
tagcomment = pd.read_csv("D:\\senior\\sentiment\\data\\tags.csv",
encoding='iso-8859-1')
tagcommentId = tagcomment['commentid']
commendId = []
cleanComment = []
sentiment = []
# tagger = []
if score > 0: return 'positive'
elif score < 0: return 'negative'
else: return 'neutral'
afinn = Afinn()
def afinn_polarity(text):
score = afinn.score(text)
if score > 0: return 'positive'
elif score < 0: return 'negative'
else: return 'neutral'
senti = PySentiStr()
senti.setSentiStrengthPath(senti_strength_jar_filepath)
senti.setSentiStrengthLanguageFolderPath(senti_strength_data_dirname)
def sentistrength_polarity(text):
score = senti.getSentiment([text])[0]
if score > 0: return 'positive'
elif score < 0: return 'negative'
else: return 'neutral'
mpqa_df = pd.read_csv(mpqa_filepath)
def mpqa_polarity(text):
#tqdm loading
from datetime import datetime
from pair_score import calculatePairScore
import os
import sys
NUMTHREAD = 20
curdir = os.getcwd()
while 'filepathhelper.py' not in os.listdir(curdir):
curdir = os.path.dirname(curdir)
sys.path.append(curdir)
import filepathhelper
from tqdm import tqdm
import multiprocessing as mp
senti = PySentiStr()
# senti.setSentiStrengthPath('C:\ProgramData\Anaconda3\Lib\site-packages\sentistrength\SentiStrength.jar')
# senti.setSentiStrengthLanguageFolderPath('C:\ProgramData\Anaconda3\Lib\site-packages\sentistrength\\')
senti.setSentiStrengthPath(
'/home/waraleetan/ming/lib/python2.7/site-packages/sentistrength/SentiStrength.jar'
)
senti.setSentiStrengthLanguageFolderPath(
'/home/waraleetan/ming/lib/python2.7/site-packages/sentistrength/')
def cleanData(text):
#remove [~]
result = re.sub("\\[~.*?\\]", "", text)
#remove{code}
result = re.sub(r'^{code(.+){code}', ' ', result)
