About Twitter Sentiment Analysis

The client has a political background, works as a public figure and has a large number of followers on social media.

Services Provide
  • Web Application
  • Web Scrapping
  • Natural Language Processing
Technologies Use
  • Python
  • Scikit-learn
  • Django
  • MSSQL Server
  • Pandas
  • Celery
  • Gensim
  • NLTK
  • Scrappy
  • BeautifulSoup
  • Selenium

Business Problem

Understand people sentiments on social media after launching different political campaigns.

Challenges

  1. User doesn't want to log in via twitter account so cannot access Twitter API to fetch tweets
  2. Tweets have different grammatical constructs and sometimes may have non-english words written using english characters
  3. Twitter has certain security measures which blocks the scrapping bots
  4. Build and adjust machine learning algorithm as it processes more tweets

Solutions

  1. Extraction - Fetch tweets using various web scraping libraries Scrappy, BeautifulSoup and Selenium.
  2. Preparation - Prepare dataframe for preprocessing the tweets to remove non-contextual words
  3. Exploration - Identify the most tweeted hashtags and most used words, Find Collocations (Words most frequently used together).
  4. Clustering - Group the tweets together by distance based on the words used.
  5. Parsing the tweet messages processed and generate the training dataset using gensim.
  6. Processing the content further and fetch the topics (Group of words) using sklearn.
  7. Identifying and categorizing in different sentiments using TextBlob and nltk.

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