Twitter Sentiment Analysis

Sentiment analysis, also known as opinion mining, refers to the use of natural language processing, text analysis and computational linguistics to systematically identify, extract, quantify, and study affective states and subjective
information. Sentiment analysis is widely applied to understand the voice of the customer who has expressed opinions on various social media platforms.

Sentiment Analysis is the analysis of the feelings (i.e. emotions, attitudes, opinions, thoughts, etc.) behind the words by making use of Natural Language Processing (NLP) tools. Natural Language Processing essentially aims to understand and create a natural language by using essential tools and techniques.

Machine learning helps to train algorithm which scanned for the positive and negative word and based on that it scores the complete sentence. So the question whether Machine learning can correctly identifies 100% of the sentences. The answer is No, most the sentiment analysis algorithms achieves only 80% accuracy due to the complexity of natural language.

Benefits of sentiment analysis

  • Optimize & adjust marketing strategy
  • Improve customer services and expedite the customer resolution process
  • Crisis Management
  • Brand perspective
  • Tracking user sentiment

OK, let’s get started. How typical tweet looks like?

A tweet is a social media message posted on Twitter.com. It is restricted to 140 characters through twitter is currently experimenting with doubling the length of the tweet from 140 characters to 280 characters. Though most tweets contain mostly text, it is possible to embed URLs, pictures, videos, vines and GIFs.

Tweets contain components called hashtags, which are words that capture the subject of the tweet. They are prefixed by the ‘#’ character. Usernames or handles of those who post are recognized by the ‘@’ symbol. A user can direct a message to another user by adding the handle, with the ‘@’ symbol.

Webapp Capabilities

  • Login/Logout & Registration page
  • Easy interface to setup the twitter API
  • Add term to database to extract twitter data
  • User friendly dashboard to visualize sentiment, hashtags, and most frequent positive and negative words.

Technology Stacks

  • Python
  • Important packages – NLTK and VanderSentiment
    for sentiment analysis
  • Django
  • Chart.js
Screenshots

6 thoughts on “Twitter Sentiment Analysis”

  1. The project works exactly as its mentioned. There were no errors and every module worked perfectly. They also provide all the instructions and dependencies to be installed. Overall , this page can be trusted and is not a spam.

  2. hello, iam currently doing research for my student project about ML. can you share your code for my reference? thank you so much.

Leave a Comment

Your email address will not be published. Required fields are marked *