Привет, поэтому у меня есть код, который транслирует твиты из API Twitter и выполняет анализ настроений на них в виде файла CSV. Я хочу, чтобы твиты загружались в базу данных SQLite перед выполнением анализа настроений. Я неплохо справился с работой go и просто не могу понять это. Я использовал документацию https://docs.python.org/3/library/sqlite3.html и просмотрел множество сообщений в стеке ( твип-поток в базу данных sqlite - недопустимый синтаксис , Python твип-запись в sqlite3 db , твипный поток в базу данных sqlite - неверный синтаксис , твипий в sqlite3 ), но я все еще не могу заставить его делать то, что мне нужно.
С моим кодом, как это в настоящее время, я получаю сообщение об ошибке: «AttributeError: объект list» не имеет атрибута «db» »
Я предположил, что .db распознается SQLite, но я предполагаю, что ' Я не могу найти стековую запись с указанной ошибкой c, хотя я совершенно потерян. Любая помощь с этим была бы удивительной, поскольку я действительно в растерянности. Часть моего кода, от которой я получаю сообщение об ошибке, будет находиться между строк ******** s:
import csv
import re
import sys
import tweepy
import matplotlib.pyplot as plt
from textblob import TextBlob
import sqlite3 as lite
global db
global cursor
class SentimentAnalysis:
def __init__(self):
self.tweets = []
self.tweetText = []
# self.api = tweepy.API(self.auth)
# Change access details below to point to own application
def download_data(self):
# authenticating
consumerKey = ''
consumerSecret = ''
accessToken = ''
accessTokenSecret = ''
auth = tweepy.OAuthHandler(consumerKey, consumerSecret)
auth.set_access_token(accessToken, accessTokenSecret)
api = tweepy.API(auth)
# input for term to be searched and how many tweets to search
searchTerm = input("Enter Keyword/Tag to search about: ")
NoOfTerms = int(input("Enter how many tweets to search: "))
# searching for tweets
self.tweets = tweepy.Cursor(api.search, q=searchTerm, lang="en").items(NoOfTerms)
# Open/create a file to append data to
csvFile = open('result.csv', 'a')
# Use csv writer
csvWriter = csv.writer(csvFile)
# creating variables to store info
polarity = 0
positive = 0
wpositive = 0
spositive = 0
negative = 0
wnegative = 0
snegative = 0
neutral = 0
# iterating through tweets fetched
for tweet in self.tweets:
# Append to temp so that we can store in csv later. I use encode UTF-8
self.tweetText.append(self.clean_tweet(tweet.text).encode('utf-8'))
# print (tweet.text.translate(non_bmp_map)) #print tweet's text
analysis = TextBlob(tweet.text)
# print(analysis.sentiment) # print tweet's polarity
polarity += analysis.sentiment.polarity # adding up polarities to find the average later
if analysis.sentiment.polarity == 0: # adding reaction of how people are reacting to find average later
neutral += 1
elif 0 < analysis.sentiment.polarity <= 0.3:
wpositive += 1
elif 0.3 < analysis.sentiment.polarity <= 0.6:
positive += 1
elif 0.6 < analysis.sentiment.polarity <= 1:
spositive += 1
elif -0.3 < analysis.sentiment.polarity <= 0:
wnegative += 1
elif -0.6 < analysis.sentiment.polarity <= -0.3:
negative += 1
elif -1 < analysis.sentiment.polarity <= -0.6:
snegative += 1
# Write to csv and close csv file
csvWriter.writerow(self.tweetText)
# csvFile.close() # delete this for the insert SQL code bit
**********************************************************************************
**********************************************************************************
con = lite.connect(self.tweetText.db)
cur = con.cursor()
cur.execute('''CREATE TABLE self.tweetText(txt text, author text, created int, source text)''')
cur.execute("INSERT INTO self.tweetText(?, ?, ?, ?)", (status.txt,
status.author,
status.created,
status.source))
con.commit()
**********************************************************************************
**********************************************************************************
# finding average of how people are reacting
positive = self.percentage(positive, NoOfTerms)
wpositive = self.percentage(wpositive, NoOfTerms)
spositive = self.percentage(spositive, NoOfTerms)
negative = self.percentage(negative, NoOfTerms)
wnegative = self.percentage(wnegative, NoOfTerms)
snegative = self.percentage(snegative, NoOfTerms)
neutral = self.percentage(neutral, NoOfTerms)
# finding average reaction
polarity = polarity / NoOfTerms
# printing out data
print("How people are reacting on " + searchTerm + " by analyzing " + str(NoOfTerms) + " tweets.")
print()
print("General Report: ")
if polarity == 0:
print("Neutral")
elif 0 < polarity <= 0.3:
print("Weakly Positive")
elif 0.3 < polarity <= 0.6:
print("Positive")
elif 0.6 < polarity <= 1:
print("Strongly Positive")
elif -0.3 < polarity <= 0:
print("Weakly Negative")
elif -0.6 < polarity <= -0.3:
print("Negative")
elif -1 < polarity <= -0.6:
print("Strongly Negative")
print()
print("Detailed Report: ")
print(str(positive) + "% people thought it was positive")
print(str(wpositive) + "% people thought it was weakly positive")
print(str(spositive) + "% people thought it was strongly positive")
print(str(negative) + "% people thought it was negative")
print(str(wnegative) + "% people thought it was weakly negative")
print(str(snegative) + "% people thought it was strongly negative")
print(str(neutral) + "% people thought it was neutral")
self.plotPieChart(positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm,
NoOfTerms)
def clean_tweet(self, tweet):
# Remove links, special characters, etc., from tweet
return ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t]) | (\w +:\ / \ / \S +)", " ", tweet).split())
# function to calculate percentage
def percentage(self, part, whole):
temp = 100 * float(part) / float(whole)
return format(temp, '.2f')
def plotPieChart(self, positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm, noOfSearchTerms):
labels = ['Positive [' + str(positive) + '%]', 'Weakly Positive [' + str(wpositive) + '%]','Strongly Positive [' + str(spositive) + '%]', 'Neutral [' + str(neutral) + '%]',
'Negative [' + str(negative) + '%]', 'Weakly Negative [' + str(wnegative) + '%]', 'Strongly Negative [' + str(snegative) + '%]']
sizes = [positive, wpositive, spositive, neutral, negative, wnegative, snegative]
colors = ['yellowgreen','lightgreen','darkgreen', 'gold', 'red','lightsalmon','darkred']
patches, texts = plt.pie(sizes, colors=colors, startangle=90)
plt.legend(patches, labels, loc="best")
plt.title('How people are reacting on ' + searchTerm + ' by analyzing ' + str(noOfSearchTerms) + ' Tweets.')
plt.axis('equal')
plt.tight_layout()
plt.show()
if __name__== "__main__":
# db_init()
sa = SentimentAnalysis()
sa.download_data()