class Conversation:
def __init__(self, path, interval="month"):
with open(path, 'r') as f:
self.__soup = BeautifulSoup(f.read(), "html.parser")
self.messages = []
self.name = self.__soup.find("title").text.replace(
"Conversation with ", "")
message_headers = self.__soup.find_all("div",
class_="message_header")
self.__span_meta = [
m.find("span", class_="meta").text for m in message_headers
]
self.__fbt = FbTime(self.__span_meta)
for m in self.__soup.find_all("div", class_="message"):
span = m.find("span", class_="meta")
self.messages.append(
Message(
m.find("span", class_="user").text,
self.__fbt.span_meta_to_date(span.text, interval),
span.text, m.next_sibling.text))
self.__sent = Sentiment(self.messages, self.__fbt)
self.participants = self.__scrape_participants()
def interaction_freq(self):
times = self.__fbt.generate_time_dict()
for date_str in self.__span_meta:
time = date_str.split("at ")[1][:5]
hour = time.split(":")[0]
times[hour + ":00"] += 1
return times
def interaction_timeline(self, name):
dates = self.__fbt.generate_date_dict()
for message in self.messages:
if message.name == name:
dates[message.date] += 1
return dates
def sentiment_timeline(self, name):
return self.__sent.sentiment_timeline(name)
def avg_sentiment(self, name):
return self.__sent.avg_sentiment(name)
def __scrape_participants(self):
users = []
for user_span in self.__soup.find_all("span", "user"):
user_name = user_span.text
if user_name not in users:
users.append(user_name)
return users
class Conversation:
def __init__(self, path, interval="month"):
with open(path, 'r') as f:
self.__soup = BeautifulSoup(f.read(), "html.parser")
self.messages = []
self.name = self.__soup.find("title").text.replace(
"Conversation with ", "")
message_headers = self.__soup.find_all("div",
class_="message_header")
self.__span_meta = [
m.find("span", class_="meta").text for m in message_headers
]
self.__fbt = FbTime(self.__span_meta)
for m in self.__soup.find_all("div", class_="message"):
span = m.find("span", class_="meta")
self.messages.append(
Message(
m.find("span", class_="user").text,
self.__fbt.span_meta_to_date(span.text, interval),
span.text, m.next_sibling.text))
self.__sent = Sentiment(self.messages, self.__fbt)
self.participants = self.__scrape_participants()
def interaction_freq(self):
return self.__fbt.interaction_freq()
def interaction_timeline(self, name):
return self.__fbt.interaction_timeline(name, self.messages)
def sentiment_timeline(self, name, interval):
return self.__sent.sentiment_timeline(name, interval)
def avg_sentiment(self, name):
return self.__sent.avg_sentiment(name)
def get24HourTime(self, elem):
return self.__fbt.get24HourTime(elem)
# Returns a list of participants in the conversation.
def __scrape_participants(self):
users = []
for user_span in self.__soup.find_all("span", "user"):
user_name = user_span.text
if user_name not in users:
users.append(user_name)
return users
