A web app that uses data from Twitter combined with sentiment analysis and emotion detection to create a series of data visualisations to illustrate the happy and less happy locations, topics and times.
This project aims to make Twitter data more understandable. It streams real-time tweets, or can fetch tweets about a specific topic or keyword - it then analyses this data using a custom-written sentiment analysis algorithm, and finally displays the results with a series of dynamic D3.js data visualisations.
The aim of the app is to allow trends to be found between sentiment and other factors such as geographical location, time of day, other topics...
It has a wide range of uses, from analysing the effectiveness of a marketing campaign, to comparing two competing topics.
As part of the documentation there is one shot of each screen in it's current state. View screen shots here
Below is a sample of the 12 key screens.
Several open source node modules have been developed and published on npm as part of this project
Prerequisites - You will need Node.js, MongoDB and git installed on your system. You will also need Gulp and Bower, which (once node is installed) you can install by running
npm install gulp bower -g. Also Yarn is recommended.
Get the files -
git clone https://github.com/Lissy93/twitter- sentiment-visualisation.git then navigate into it with
Install dependencies -
yarn will install the npm node_modules, then should automatically kick off a
bower install (if not, then just run it manually). If you are developing, you will need to use
npm install in order to get the devDependencies too.
yarn run config will generate the
config\src\keys.coffee file, which you will then need to populate with your API keys and save. Also check that your happy with the general app config in
Build Project -
yarn run build will compile the project, from the source.
Run the project - Run
yarn start then open your browser and navigate to http://localhost:8080
To run the tests:
npm test or see the more test strategy
To run in development mode, use
yarn run dev. This will use dev environmental variables, and also will watch for changes, lint, compile and refresh automatically.
TSV uses the Gulp streaming build tool to automate the development workflow.
The key tasks you need to run are:
gulp generate-config- before first-time running of the project, run this command to generate configuration files for your API keys
gulp nodemon- Runs the application on the default port (probably 8080)
gulp test- This will run all unit and coverage tests, printing a summary of the results to the console and generating more detailed reports into the reports directory.
gulp- this is the default task, it will check the project is configured correctly, build ALL the files, run the server, watch for changes, recompile relevant files and reload browsers on change, and keep all browsers in sync, when a test condition changes it will also re-run tests - a lot going on!
To read more about the project setup and gulp process, see build environment in the docs
Twitter Sentiment Visualisation follows the TDD approach and is structured around it's unit tests.
To run tests:
More details on each of the tools and how they will be implemented along with the pass and fail criteria can be found on the test strategy page of the documentation.
This project wouldn't have been possible at all without making use of many many open source packages, libraries, frameworks etc..
I would like to personally thank the hundreds of developers who have worked on open source packages like these.
There is an extensive stack of technologies that were used to develop the final application. The following list is a summary of the key utilities:
Read more about the application here.
The application is fully documented, which can be viewed here
A live demo of the application has been deployed to: http://sentiment-sweep.com
Source Code: https://github.com/Lissy93/twitter-sentiment-visualisation
Twitter is a microblogging website that allows users to share their opinion, facts, and so on via tweets. People can follow their favourite celebrities and other famous personalities on the Twitter app. They will receive tweets they have posted in their feeds. At present, Twitter has over 100 million active users and 500 million tweets are shared daily. While investing in Twitter clone app, make sure that it has the following features:
Users can gain more attention to a particular tweet by pinning the tweet on top of their profiles. Whenever people visit your page, pinned tweets will be visible to them. It is one of the best strategies to get more attention to blogs, business, promotional products & services, and many more.
Twitter moments are curated stories and posts that belong to specific categories. Users post content related to an event like a music concert, theatre play, and so on. This Twitter clone script feature brings more followers to the users’ accounts.
Users get alerts whenever new content is posted by the followers, upcoming features, for important events, etc. They can also customize the category which they receive as alerts like they can mute from certain followers according to their preference.
Create photo collages
Photos are an essential part of any social networking site, and the Twitter app clone is no exception. Users can create a collage with a maximum of four photos during tweeting through this photo collage feature.
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Social networking sites are topping the charts both in user engagements and revenue. Among the top-grossing platforms, apps like Facebook, Twitter, Instagram, etc., occupy a firm and commendable position. With almost 80% of the Internet users establishing a social media presence, entrepreneurs needn’t hesitate to invest in a Twitter Clone App.
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Twitter is one of the widely used apps with some of the most attractive features. The uniqueness of the app captures more attention of users. People make use of Twitter to post trending information and follow their favourite celebrities and personalities. Developing a Twitter clone would be a great choice, as there are no other apps that resemble this popular social media platform. Here are some of the remarkable features one should incorporate into a Twitter clone.
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Sentiment analysis is one of the most common tasks in Data Science and AI. In this article, we will use Python, Tweepy and TextBlob to perform sentiment analysis of a selected Twitter account using Twitter API and Natural Language Processing.
We will use Twitter to perform sentiment analysis of the written text. We will use Twitter in this example but this can be also used in a business context to analyse different social media accounts, reviews of your company, reviews of your products and services, analysing support tickets, emails or free text from surveys to get an idea of the mood that is coming from people engaging with you and your business online.
You will learn how to perform basic sentiment analysis using TextBlob; powerful Natural Language Processing library for Python. We will also use the WordCloud library to visualise some of our findings and we will also work with a Twitter API. Familiarizing with APIs is a useful skill for data scientists. It is a very common method of getting hold of the data from the internet.
Our task is to analyse the Tweets of an individual Twitter account in terms of Subjectivity and Polarity. We will identify individual tweets as positive, negative and neutral and calculate the percentage of positive tweets. We will use the WordCloud library to display a word cloud of the most positive words from the tweets.
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