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!")
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)
#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) except: return 0
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)
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) main_frame = pd.concat(li, axis=0, ignore_index=True)
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 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 #number of users tagged
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 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 👋')
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 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)
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)
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 = [] # tagee = [] countnon = 0
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): tokens = word_tokenize(text)
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) #remove{function name} result = re.sub(r'\s\w+\(\)', ' ', result)