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You’ll also learn some advanced language features that recently have become more common in Python code.

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IoT Trends: A few key trends emerged based on the tweet data. The most frequently tweeted words included “wearables,” “smarthome,” “mobile,” “smarter cities,” and “big data.”

Sentiment: In order to determine how those included in the tweet data are reacting to an increasingly digital lifestyle, I conducted a sentiment analysis based on my own interpretation of the tweet. (You get really good at distinguishing genuine positivity from snark in my job.) I found 62 percent of the tweets were positive, 31 percent were neutral, and 7 percent were

 

 

Here’s a look at the Python code I used to stream tweets:

 

import encoding_fix

 

import tweepy

 

from twitter_authentication import CONSUMER_KEY, CONSUMER_SECRET, ACCESS_TOKEN, ACCESS_TOKEN_SECRET

 

auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)

 

auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)

 

api = tweepy.API(auth)

 

output_file = open(“InternetofThings.csv”, “w”)

 

class StreamListener(tweepy.StreamListener):

 

def on_status(self, tweet):

 

modified_tweet = tweet.text

 

modified_tweet = modified_tweet.replace(“\n”, ” “)

 

modified_tweet = modified_tweet.replace(“,”, “”)

 

output_file.write(‘,’.join([tweet.author.screen_name, str(tweet.created_at), modified_tweet, str(tweet.retweet_count)]) + ‘\n’)

 

print(modified_tweet)

 

def on_error(self, status_code):

 

print(‘Error: ‘ + repr(status_code))

 

return False

 

l = StreamListener()

 

streamer = tweepy.Stream(auth=auth, listener=l)

 

keywords = [‘#InternetofThings’, ‘#IoT’]

 

streamer.filter(track = keywords)

 

output_file.close()

 

And, here’s the Python code I used to pull 1,000 tweets:

 

import encoding_fix

 

import tweepy

 

from twitter_authentication import CONSUMER_KEY, CONSUMER_SECRET, ACCESS_TOKEN, ACCESS_TOKEN_SECRET

 

import time

 

auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)

 

auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)

 

api = tweepy.API(auth)

 

output_file = open(“InternetofThings_pull.csv”, “w”)

 

counter = 0

 

for page in tweepy.Cursor(api.search, ‘#InternetofThings #IoT’, count=100).pages():

 

counter = counter + len(page)

 

for tweet in page:

 

modified_tweet = tweet.text

 

modified_tweet = modified_tweet.replace(“\n”, ” “)

 

modified_tweet = modified_tweet.replace(“,”, “”)

 

output_file.write(‘,’.join([tweet.author.screen_name, str(tweet.created_at), modified_tweet, str(tweet.retweet_count)]) + ‘\n’)

 

# end this loop if we’ve gotten 1000

 

if counter == 1000:

 

break

 

# This page suggests we can do one request every 5 seconds:

 

# https://dev.twitter.com/rest/reference/get/search/tweets

 

time.sleep(5)

 

output_file.close()

 

From those Python codes I learned the following about IoT:

 

A succinct definition: The “Internet of Things” is the term used to describe the connectivity between technology and physical objects.

Who’s leading the conversation: Based on the tweet data, brands are dominating the online conversation over individuals, and it’s not hard to see why. Think wearable fitness trackers, smart watches, on-demand buttons to reorder household products, etc.

Deloitte Digital Senior Community Manager Laura Anderson shares how she used Python codes to monitor online community discussions and research topics, in this case the Internet of Things.

 

Vijetha