v-ishwa-jeet/jobtweets

:mag: This project is about searching the twitter for job opportunities using popular hashtags and applying sentiment analysis on this.

Job Opportunities using Twitter

Twitter Sentiment Analysis using Python

The project is about searching the twitter for job opportunities using popular #hashtags and applying sentiment analysis on this.

Few popular hashtags -

#Jobs #Careers #JobOpening

#FreshHiring #Recruitments #JobOpportunities

Motivation

Twitter is all about enabling users to send out brief messages to large audiences. If you haven’t been taking advantage of Twitter as a job search tool, it’s time to jump in. When used intelligently, Twitter can have a profound impact on your job search success – or lack thereof. Small steps can help you turn Twitter into your own personal job search platform. Try them today and see what a difference they make in your overall job search success.

About the Project

What is Sentiment Analysis?

Sentiment Analysis is the process of ‘computationally’ determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker.

Steps involved in this project

3 major steps in jobtweets.py code :

1. Authorize twitter API client.
2. Make a GET request to Twitter API to fetch tweets for a particular query.
3. Parse the tweets. Classify each tweet as positive, negative or neutral.

Explanation

• First of all, I've created a TwitterClient class. This class contains all the methods to interact with Twitter API and parsing tweets. We use __init__ function to handle the authentication of API client.

• In get_tweets function, I have used fetched_tweets = self.api.search(q = query, count = count) to call the Twitter API to fetch tweets. 'query' is basically, the hashtags.

• In get_tweet_sentiment I've used textblob module. analysis = TextBlob(self.clean_tweet(tweet))

• clean_tweet method to remove links, special characters, etc. from the tweet using some simple regex.

• I have used sentiment.polarity method of TextBlob class to get the polarity of tweet between -1 to 1.

if analysis.sentiment.polarity > 0:
       return 'positive'
elif analysis.sentiment.polarity == 0:
       return 'neutral'
else:
       return 'negative'

• Finally, I've printing the percentage of positive, negative and neutral tweets about a #hashtag(query).

Note - You can change the hashtags by changing query = 'WRITE YOUR OWN HASHTAG'

tweets = api.get_tweets(query = 'Job Opportunities', count = 500)

Libraries Used

• Tweepy - tweepy is the python client for the official Twitter API.
• TextBlob - textblob is the python library for processing textual data.

Installation

• Install Tweepy using pip command: pip install tweepy
• Install TextBlob using pip command: pip install textblob

How to run?

• Get started with Twitter API by signing up for Twitter Developer Account.
• In order to fetch tweets through Twitter API, you need to register an App through your twitter account.
• Follow this link to register your app.
• Get the API keys. Need help, follow this link
• Open jobtweets.py and replace 'XXXXXXXXXXXX' with your API keys.

        consumer_key = 'XXXXXXXXXXXX'
        consumer_secret = 'XXXXXXXXXXXX'
        access_token = 'XXXXXXXXXXXX'
        access_token_secret = 'XXXXXXXXXXXX'

• Run python jobtweets.py
• It may take a minute to fetch the results from Twitter. Make sure that you've proper internet connection.

Project Reports

• Download the presentation.
• Download project report.

Useful Links

• Getting started with Twitter Developer Platform
• How to Install PIP for Python on Windows, Mac and Linux

Related Work

Twitter Sentiment Analyzer - A web app to search the keywords(Hashtags) on Twitter and analyze the sentiments of it. The source code is written in PHP and it performs Sentiment Analysis on Tweets by using the Datumbox API.

Contributing

• Clone this repository:

git clone https://github.com/vinitshahdeo/jobtweets.git

• Check out any issue from here.

• Make changes and send Pull Request.

Need help?

📧 Feel free to contact me @ vinitshahdeo@gmail.com

License

MIT © Vinit Shahdeo