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Twitter’s 139 million daily active users interact with brands on the network in important ways, from retweeting your content to a broader audience to making purchases that directly impact your bottom line. In this Project, I have performed Shinzo Abe (Japanese Prime Minister) Twitter Sentiment Analysis.

Home Page: https://kanishksh4rma.github.io

License: MIT License

Jupyter Notebook 100.00%
nlp shinzo-abe twitter-sentiment-analysis natural-language-processing data-science data-visualization sentiment-analysis machine-learning

shinzo-abe-twitter-sentiment-analysis's Introduction

Shinzo-Abe-twitter-Sentiment-analysis

Open Source Love Code Climate

Twitter’s 139 million daily active users interact with brands on the network in important ways, from retweeting your content to a broader audience to making purchases that directly impact your bottom line. If you’re not using Twitter analytics, you’re missing out on key Twitter insights that could help you refine your strategy and maximize ROI.

Objective

  • Understand Contextual understanding and tone
  • Visualize the sentiments behind the tweets.

Installation

Copy and Run this in terminal:

pip install pandas textblob wordcloud seaborn

git clone https://github.com/kanishksh4rma/Shinzo-Abe-twitter-Sentiment-analysis.git

demo_install

Libraries used :

  * pandas
  * textblob
  * wordcloud
  * seaborn
  * numpy
  * matplotlib

Screenshots

Screenshot 4

Screenshot 3

Screenshot 2

Screenshot 1

About Dataset

This dataset contains Japanese Prime Minister Tweet. Japanese culture, diplomatic problem ( North Korea and Tramp etc), time of disaster, economics…

For example,14.April 2014 "Removing radiation contaminated water in all weather, 365/24 at Fukushima. I am deeply thankful for dedication and commitment of our peers." Maybe if you analyze his tweets about Japanese economy this data will be useful for stock price forecasting etc.

Dataset Source : Kaggle

Developed by : Kanishk sharma

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