Hello, Guys,
In this tutorial, I will guide you on how to perform sentiment analysis on textual data fetched directly from Twitter about a particular matter using tweepy and textblob.
what is sentiment analysis?
Sentiment analysis is a process of analyzing emotion associated with textual data using natural language processing and machine learning techniques.
If you’re new …
If you’re new to sentiment analysis in python I would recommend you watch emotion detection from the text first before proceeding with this tutorial.
what are we going to build ..
We are going to build a python command-line tool/script for doing sentiment analysis on Twitter based on the topic specified.
How will it work ?
You will just enter a topic of interest to be researched in twitter and then the script will dive into Twitter, scrap related tweets, perform sentiment analysis on them and then print the analysis summary.
Project requirements
To follow through tutorial you need the following
- Sign up for twitter to Developers to get API Key
- Install tweepy Libray (Twitter functionalities)
- Install TextBlob Library (Natural language processing)
Twitter Developer (API KEYS)
You can follow through this link Signup in order to signup for twitter Developer Account to get API Key.
Once you signup for a developer account and apply for Twitter API, It might take just a few hours to a few days to get approval.
After being approved Go to your app on the Keys and Tokens page and copy your api_key and API secret key in form as shown in the below picture.
Tweepy Installation
The easiest way to install the latest version from PyPI is by using pip:
$ pip install tweepy
You can also use Git to clone the repository from GitHub to install the latest development version:
$ git clone https://github.com/tweepy/tweepy.git $ cd tweepy $ pip install .
TextBlob Installation
$ pip install -U textblob $ python -m textblob.download_corpora
Building our Twitter analysis tool
Now after everything is clearly installed, let’s get hand dirty by coding our tool from scratch.
Put API_KEYS on separate file
First of all I have separated project into two files , one consisting api keys while others consisting our code for script .
. ├── API_KEYS.py └── app.py 0 directories, 2 files
If we look inside the API_KEYS.py it look as shown below whereby the value of api_key and api_secret_key will be replaced by your credentials received from twitter
API_KEYS.py
api_key = 'your api key' api_secret_key = 'your api secret key'
Now Let’s start coding our script
Importing Libraries and dependencies
from tweepy import API, OAuthHandler from textblob import TextBlob from API_KEYS import api_key, api_secret_key
Authenticating our script
To start fetching tweets from twitter, firstly we have to authenticate our app using api key and secret key.
To authenticate our api we will use OAuthHandler as shown below
authentication = OAuthHandler(api_key, api_secret_key) api = API(authentication)
Fetching tweets on Particular Topic
To fetch tweets from twitter using our Authenticated api use search method fetch tweets about a particular matter .
#________Syntax __________________ public_tweets = api.search(Topic)
public_tweets is an iterable of tweets objects but in order to perform sentiment analysis we only require the tweet text.
Therefore in order to access text on each tweet we have to use text property on tweet object as shown in the example below.
from tweepy import API, OAuthHandler
from textblob import TextBlob
from API_KEYS import api_key, api_secre
authentication = OAuthHandler(api_key, api_secret_key)
api = API(authentication)
corona_tweets = api.search('corona virus')
for tweet in corona_tweets:
text = tweet.text
print(text)When you run the above script it will produce the result similar to what shown below .
...... RT @amyklobuchar: So on Frontier Airlines you now have to pay an extra fee to keep yourself safe from corona virus. As I said today at the… There are so many disturbing news these days ON TOP OF CORONA VIRUS. It just sinks my heart😟 We all need therapy. RT @ug_chelsea: Corona virus symptoms basically are the same feelings you get when your wife is checking your phone
Performing Sentiment analysis
Now Let’s use use TextBlob to perform sentiment analysis on those tweets to check out if they are positive or negative
Textblob Syntax to checking positivity or negativity
polarity = TextBlob(Text).sentiment.polarity ''' If polarity is less than 0 it's negative If polarity is greater than 0 it's positive '''
Final Application
I then compiled the above knowledge we just learned to building the below script with addition of clean_tweets function to remove hashtags in tweets
app.py
from tweepy import API, OAuthHandler
from textblob import TextBlob
from API_KEYS import api_key, api_secret_key
def clean_tweets(tweet):
tweet_words = str(tweet).split(' ')
clean_words = [word for word in tweet_words if not word.startswith('#')]
return ' '.join(clean_words)
def analyze(Topic):
positive_tweets, negative_tweets = [], []
authentication = OAuthHandler(api_key, api_secret_key)
api = API(authentication)
public_tweets = api.search(Topic, count=10)
cleaned_tweets = [clean_tweets(tweet.text) for tweet in public_tweets]
for tweet in cleaned_tweets:
tweet_polarity = TextBlob(tweet).sentiment.polarity
if tweet_polarity<0:
negative_tweets.append(tweet)
continue
positive_tweets.append(tweet)
return positive_tweets, negative_tweets
positive, negative = analyze('Magufuli')
print(positive , '\n\n', negative)
print(len(positive), ' VS ', len(negative))To change a Topic you want to analyze or change Topic parameter in in analyze function to Topic you want.
Also you can specify the number of tweets to be fetched from twitter by changing the count parameter .
When you run the above application it will produce results to what shown below
..................... ['@o_abuga Obvious, the test kits the results are doubtful!! Magufuli said it'] 9 VS 1
======================The end ==================================
Hope you find it interesting, now don’t forget to subscribe to this blog to stay updated on upcoming python tutorial.
I also recommend you to read this;
- How to translate languages using Python
- Emotion detection from the text in Python
- 3 ways to convert text to speech in Python
- How to perform speech recognition in Python
- Make your own Plagiarism detector in Python
- Learn how to build your own spam filter in Python
- Make your own knowledge-based chatbot in Python
- How to perform automatic spelling correction in Python
To get he full code for this article check it out on My Github
