def __init__(self, output_dir, verbose=False):
"""
:param output_dir: Absolute filepath to output directory.
:param verbose: boolean: Write info to console.
:return:
"""
self.dbmodel = BasicDbModel()
self.s_analyzer = LexiconSentimentAnalyzer()
self.text_writer = TextWriter(output_dir) # writing CSV files
self.verbose = verbose # verbose output
self.stock_processor = StockPriceProcessor() # Object for price movements
self.source_metrics_calculator = SourceMetricsCalculator(output_dir)
self.total_metrics_calculator = TotalMetricsCalculator(output_dir)
self.source_metrics_calculator_2_classes = SourceMetricsCalculator2classes(output_dir)
class DocumentsAnalyzer(object):
def __init__(self, output_dir, verbose=False):
"""
:param output_dir: Absolute filepath to output directory.
:param verbose: boolean: Write info to console.
:return:
"""
self.dbmodel = BasicDbModel()
self.s_analyzer = LexiconSentimentAnalyzer()
self.text_writer = TextWriter(output_dir) # writing CSV files
self.verbose = verbose # verbose output
self.stock_processor = StockPriceProcessor() # Object for price movements
self.source_metrics_calculator = SourceMetricsCalculator(output_dir)
self.total_metrics_calculator = TotalMetricsCalculator(output_dir)
self.source_metrics_calculator_2_classes = SourceMetricsCalculator2classes(output_dir)
## Analyze output file
def analyze_all_companies(self, from_date, to_date, file_name, price_type, const_boundaries, used_dict_name='vader', classes_count=3):
"""
Analyze all documents for all companies.
:param from_date:
:param to_date:
:param file_name:
:param price_type:
:param used_dict_name:
:return:
"""
# Reset files.
self.text_writer.write_econometric_file(file_name, [self._get_days_stats_header()], 'w')
total_m_header = self.total_metrics_calculator.get_total_metrics_header()
self.text_writer.write_econometric_file(file_name + '_total-metrics', [total_m_header], 'w')
source_m_header = self.source_metrics_calculator.get_source_metrics_header()
self.text_writer.write_econometric_file(file_name + '_source-metrics', [source_m_header], 'w')
# Process companies
companies = self.dbmodel.get_companies_order_by_total_documents(from_date, to_date)
for comp in companies:
print("<<<<<Company %d>>>>>") % comp['id']
if not self.verbose:
with FaCommon.Helpers.suppress_stdout():
self.analyze_company(comp['id'], from_date, to_date, file_name, price_type, const_boundaries, used_dict_name, False, classes_count)
else:
self.analyze_company(comp['id'], from_date, to_date, file_name, price_type, const_boundaries, used_dict_name, False, classes_count)
print('>>>All stuff saved.')
def analyze_company(self, company_id, from_date, to_date, file_name, price_type, const_boundaries, used_dict_name, write_header=False, classes_count=3):
"""
Analyze documents about company (from_date -> present date).
:return: list of days, where every row contains information for documents for this day.
"""
# Prepare variables.
examined_date = from_date
last_date = to_date
total_data = []
max_sent = float('-inf')
# Set stock prices for this company ID.
self.stock_processor.set_stock_prices(company_id, examined_date, price_type)
#exit(self.stock_processor.get_price_movement_with_delay(examined_date, 2))
# Prepare list for writing to a file.
# For every day (from "from_date" to "to_date"), query the DB for documents created on the day.
while examined_date <= last_date:
print("===%s===") % examined_date
# For every document type, process all documents and count number of neutral, positive, negative documents.
yahoo_values = self._process_yahoo(company_id, examined_date, used_dict_name)
fb_p_values = self._process_fb_posts(company_id, examined_date, used_dict_name)
fb_c_values = self._process_fb_comments(company_id, examined_date, used_dict_name)
tw_values = self._process_tweets(company_id, examined_date, used_dict_name)
# Save acquired data
day_data = [
company_id,
examined_date.strftime('%d.%m.%Y'),
fb_p_values['neu'], fb_p_values['pos'], fb_p_values['neg'],
fb_c_values['neu'], fb_c_values['pos'], fb_c_values['neg'],
yahoo_values['neu'], yahoo_values['pos'], yahoo_values['neg'],
tw_values['neu'], tw_values['pos'], tw_values['neg'],
]
# Get stock price movement direction for 1,2,3 days from examined date. Also for previous day.
day_data.append(self.stock_processor.get_price_movement_with_delay(examined_date, -1, const_boundaries))
day_data.append(self.stock_processor.get_price_movement_with_delay(examined_date, 1, const_boundaries))
day_data.append(self.stock_processor.get_price_movement_with_delay(examined_date, 2, const_boundaries))
day_data.append(self.stock_processor.get_price_movement_with_delay(examined_date, 3, const_boundaries))
# Calculate simple sentiment for all sources.
fb_post_s = self._calc_source_sentiment(fb_p_values)
fb_comment_s = self._calc_source_sentiment(fb_c_values)
yahoo_s = self._calc_source_sentiment(yahoo_values)
twitter_s = self._calc_source_sentiment(tw_values)
day_data.extend([fb_post_s, fb_comment_s, yahoo_s, twitter_s])
# Calculate overall sentiment for the day.
(max_sent, day_sent) = self._calc_overall_sentiment_for_day(max_sent, fb_p_values, fb_c_values, yahoo_values, tw_values)
day_data.append(day_sent)
# Save day data to total data.
total_data.append(day_data)
# Increment examined date.
examined_date = examined_date + datetime.timedelta(days=1)
# Normalize sentiment values.
for i, day_data in enumerate(total_data):
norm_sent = self._normalize_sentiment(total_data[i][-1], max_sent)
string_sent = self._format_sentiment(norm_sent)
total_data[i][-1] = string_sent
# Write results to file.
if write_header:
total_data.insert(0, self._get_days_stats_header())
self.text_writer.write_econometric_file(file_name, total_data, 'w')
del(total_data[0])
else:
self.text_writer.write_econometric_file(file_name, total_data, 'a')
# Calculate metrics by source.
m_filename = file_name + '_source-metrics'
if classes_count == 3:
self.source_metrics_calculator.calculate_metrics_by_source(company_id, total_data, m_filename, price_type, write_header)
else:
self.source_metrics_calculator_2_classes.calculate_metrics_by_source(company_id, total_data, m_filename, price_type, write_header)
# Calculate total metrics.
m_filename = file_name + '_total-metrics'
self.total_metrics_calculator.calculate_total_metrics(company_id, total_data, m_filename, price_type, write_header)
#### PRIVATE methods for processing documents
def _process_fb_posts(self, company_id, examined_date, used_dict_name='vader'):
# Select all FB posts for given company created on given date.
posts = self.dbmodel.get_daily_fb_posts(company_id, examined_date)
counter = {'pos': 0, 'neu': 0, 'neg': 0}
# Calculate sentiment for all posts
for post in posts:
#print("FB post: %s") % post['id'],
post_text = TextProcessing.process_facebook_text(post['text'])
if len(post_text) == 0:
continue # skip empty posts
sent_value = self.s_analyzer.calculate_vader_sentiment(used_dict_name, post_text, False)
polarity = self.s_analyzer.format_sentiment_value(sent_value)
counter[polarity] += 1
#print("| %s ... %s") % (str(round(sent_value, 4)), polarity)
# result
return counter
def _process_fb_comments(self, company_id, examined_date, used_dict_name='vader'):
# Select all FB comments.
comments = self.dbmodel.get_daily_fb_comments(company_id, examined_date)
counter = {'pos': 0, 'neu': 0, 'neg': 0}
# Calculate sentiment for all posts
for com in comments:
#print("FB comment: %s") % com['id'],
com_text = TextProcessing.process_facebook_text(com['text'])
if len(com_text) == 0:
continue # skip empty comments
sent_value = self.s_analyzer.calculate_vader_sentiment(used_dict_name, com_text, False)
polarity = self.s_analyzer.format_sentiment_value(sent_value)
counter[polarity] += 1
#print("| %s ... %s") % (str(round(sent_value, 4)), polarity)
# result
return counter
def _process_yahoo(self, company_id, examined_date, used_dict_name='vader'):
# Select all Yahoo Finance articles.
articles = self.dbmodel.get_daily_articles(company_id, examined_date)
counter = {'pos': 0, 'neu': 0, 'neg': 0}
# Calculate sentiment for all articles
for art in articles:
#print("Yahoo article: %s") % art['id'],
art_text = TextProcessing.process_article_text(art['text'])
if len(art_text) == 0:
continue # skip empty articles
sent_value = self.s_analyzer.calculate_vader_sentiment(used_dict_name, art_text, True)
polarity = self.s_analyzer.format_sentiment_value(sent_value)
counter[polarity] += 1
#print("| %s ... %s") % (str(round(sent_value, 4)), polarity)
# result
return counter
def _process_tweets(self, company_id, examined_date, used_dict_name='vader'):
# Select all Yahoo Finance articles.
tweets = self.dbmodel.get_daily_tweets(company_id, examined_date)
counter = {'pos': 0, 'neu': 0, 'neg': 0}
# Calculate sentiment for all articles.
for tw in tweets:
#print("Tweet: %s") % tw['tw_id'],
tw_text = TextProcessing.process_facebook_text(tw['text'])
if len(tw_text) == 0:
continue # skip empty tweets
sent_value = self.s_analyzer.calculate_vader_sentiment(used_dict_name, tw_text, False)
polarity = self.s_analyzer.format_sentiment_value(sent_value)
counter[polarity] += 1
#print("| %s ... %s") % (str(round(sent_value, 4)), polarity)
# result
return counter
## PRIVATE methods for determining sentiment of the whole day
def _calc_source_sentiment(self, s_dict):
"""
Calculate sentiment for given source dictionary.
:param s_dict: dictionary (sentiment -> number of documents}
:return: string (pos, neg, neu)
"""
max_s = max(s_dict.keys(), key=lambda k: s_dict[k])
# If neutral value is also the biggest one, choose it.
if s_dict['neu'] == s_dict[max_s]:
return 'neu'
return max_s
@staticmethod
def _calc_overall_sentiment_for_day(max_sent, fb_p_values, fb_c_values, yahoo_values, tw_values):
# Calculate numeric sentiment
fb_p_sent = fb_p_values['pos'] - fb_p_values['neg']
fb_c_sent = fb_c_values['pos'] - fb_p_values['neg']
yahoo_sent = yahoo_values['pos'] - fb_p_values['neg']
tw_sent = tw_values['pos'] - fb_p_values['neg']
overall_sent = fb_p_sent + fb_c_sent + yahoo_sent + tw_sent
#print fb_p_sent,fb_c_sent,yahoo_sent,tw_sent
# Is the new sentiment larger than current largest one?
if overall_sent > max_sent:
max_sent = overall_sent
return max_sent, overall_sent
@staticmethod
def _normalize_sentiment(score, alpha=100):
"""
Normalize the score to be between -1 and 1 using an alpha that approximates the max expected value.
"""
try:
norm_score = score/math.sqrt((score*score) + alpha)
except ZeroDivisionError:
norm_score = score
return norm_score
@staticmethod
def _format_sentiment(norm_score):
if -0.1 < norm_score < 0.1:
return 'neu'
elif norm_score > 0:
return 'pos'
elif norm_score < 0:
return 'neg'
@staticmethod
def _get_days_stats_header():
header_days = [
'company_id', 'date',
'fb_post_neutral', 'fb_post_positive', 'fb_post_negative',
'fb_comment_neutral', 'fb_comment_positive', 'fb_comment_negative',
'yahoo_neutral', 'yahoo_positive', 'yahoo_negative',
'twitter_neutral', 'twitter_positive', 'twitter_negative',
'stock_dir_-1', 'stock_dir_1', 'stock_dir_2', 'stock_dir_3',
'sentiment_fb_post', 'sentiment_fb_comment', 'sentiment_yahoo', 'sentiment_twitter',
'overall_sentiment',
]
return header_days