priyansh19 / Suggestion_Mining_Using_Twitter_Data

Sentiment Analysis is the automated process of analyzing text data and sorting it into sentiments positive, negative or neutral. With more than 321 million active users, sending a daily average of 500 million Tweets, Twitter allows businesses to reach a broad audience and connect with customers without intermediaries.

📝 Suggestion_Mining_Using_Twitter_Data

Suggestion Mining on any keyword/hashtag passed by user and also this code generates a csv file at last as a report

Dependencies needed to be installed before running this program in your python shell :

    1- tweepy //library to connect twitter api with code
    2- textblob //library with sentimental analysis feature
    3- matplotlib //module used to plot graph
    4- csv //module for csv files
    5- re //module for regular expressions

🌈 Basics ..

This is very basic implementation of Twitter analysis on any string of words also with a low level implementation of tkinter library in python for building its gui part

Steps for creating Twitter Api:

https://drive.google.com/file/d/1V3iLbH5n-F_i4xGS9WWBKWJMWs1q1PmM/view?usp=sharing

Installation

Download or Clone the repo, Navigate to the directory containing the files and run

python setup.py install

or if you have different versions of python installed then

python3 setup.py install 

to install the dependencies.

💡 How to Contribute..

Yes, We do accept Contrbutions.

Follow these steps :

• Fork it
• Create your feature branch: git checkout -b my-new-feature
• Commit your changes: git commit -am 'Add some feature'
• Push to the branch: git push origin my-new-feature
• Submit a pull request

💡 Challenge:

if you are reading this file try to combine all the three windows into a single window to display the general results also try to open image in the same section of the window to display the graph(pie chart).😉

Author:

Priyansh Gupta