twitterAnalysis

For complete code walk-through and analysis - Notebook link : https://drive.google.com/file/d/1V8Sl8WW_X7GuOuM-lJZ7oSwy0zfDF1jP/view?usp=sharing

Step 1: Get tweets.

To understand and estimate user sentiments over twitter to real news, we first need a lot of tweets. Our first task is to get as many as 10K tweets.

Instead of going for 10K tweets, I first got 500 tweets for sample. These tweets were searched specifically for "#modi", "#commonwealth", "#facebook", "#music."

Using the library tweepy, I have acquired the specified number of tweet. These tweets were subsequently pushed to the mongoDB using script.

These data sets are present in the folder Datasets.

Step 2: Clean the data.

Once the data sets are cleaned using script the cleaned data sets are generated.

Step 3: Analyze the data.

The data sets are then used to analyze sentiments. The top entities in tweets are found to be ['KXIPvCSK', 'TreCru', 'Syria', 'PremiosMTVMiaw', 'Hearties']. News realted to these topics is collected using news api. The resulting news articles are saved in json format.

Step 4: Presenting the analysis.

These sentiment values are plotted in python using libraries matplotlib, mplleaflet. And the resulting graphs can be seen in html notebook results.

Step 5: Hosting the results.

To present the work in web format, I have generated json files. The website is hosted at.