Natural Language Tool Kit – Tutorial 20

galiquis 6 February 2020

Twitter sentiment analysis

First up the Twitter API module needed installing:- 

galiquis@raspberrypi: $ pip3 install tweepy

Next a Twitter App is required from this link:-

https://developer.twitter.com/en/apps

This required setting up a developer account – with more justification in the application form than I was expecting – especially around what I’d be using the app for….anyway once generated it gave a live stream of twitter based on this code:-

from tweepy import Stream
from tweepy import OAuthHandler
from tweepy.streaming import StreamListener

#consumer key, consumer secret, access token, access secret.
ckey="6ru23AnzOKAieH4eYXF0XuTPS"
csecret="74Oz560aRCfo5QzzXu2I0gfOm58qkNPfZx0oSl3tnWEnEND4ex"
atoken="241873929-QkQ1eN0Du1Cg6el6rJa3sMGRHBaiSp7Cxekq61Of"
asecret="hf0ECMVfcqlPWkgOGKeNNTU1m41QQuiTOLzktsiNqqIxD"

class listener(StreamListener):

    def on_data(self, data):
        print(data)
        return(True)

    def on_error(self, status):
        print(status)

auth = OAuthHandler(ckey, csecret)
auth.set_access_token(atoken, asecret)

twitterStream = Stream(auth, listener())
twitterStream.filter(track=["car"])

https://pythonprogramming.net/twitter-api-streaming-tweets-python-tutorial/

The below covers a few tweaks with the output of the sentiment engine being saved off into a text file. 

from tweepy import Stream
from tweepy import OAuthHandler
from tweepy.streaming import StreamListener
import json
import sentiment_mod as s

#consumer key, consumer secret, access token, access secret.
ckey="*"
csecret="*"
atoken="*"
asecret="*"

class listener(StreamListener):

    def on_data(self, data):
        all_data = json.loads(data)
        tweet = all_data["text"]
        sentiment_value, confidence = s.sentiment(tweet)
        print(tweet, sentiment_value, confidence)

        if confidence*100 >= 80:
            output = open("twitter-out.txt", "a")
            output.write(sentiment_value)
            output.write('\n')
            output.close()

        return(True)

    def on_error(self, status):
        print(status)

auth = OAuthHandler(ckey, csecret)
auth.set_access_token(atoken, asecret)

twitterStream = Stream(auth, listener())
twitterStream.filter(track=["car"]) # term searched for in tweets

Next we’ll look at graphing this data.

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