## Inspiration

The solution for evaluating data in real time which help business perform better and keep track of all relevant content about a brand in real-time, perform analysis as topics or issues emerge, and detect anomaly with alert. By monitoring brand mentions on Social media brands could inform engagement and deliver better experiences for their customers across the world.

## What it does

An end-to-end real-time Twitter monitoring system to track customer behaviors towards brands by identifying sentiments fluctuation, analyzing trending topics and geographic segmentation, and detecting anomaly on scandals, with consideration of improving brands customer engagement and retention.

## How I built it

1. Extract streaming Twitter Data, preprocess data in Python, and load data into MySQL for storage
2. Perform exploratory data analysis using Pandas, NLTK, Seaborn to explore the insights
3. Connect with Plotly for real-time interactive dashboard based on time series

## Challenges I ran into

1. Unstructured tweet texts may contain messy code and emoji characters
2. Some brands may take a long time to collection enough data to perform analysis on issue emerging since they target specify groups of people
3. Some hot words will uncover useful insights only after appearing more than 10k times on tweets
4. Plotly doesn't have well-document on reference making customize dashboard much harder

## Accomplishments that I'm proud of

I'm proud that I was able to do data analysis in real time
I'm proud that I was able to put forward my ideas and come up with on spot decisons

## What I learned

I learned how Natural Language Processing works.
Learned how to allocate work to team members and make sure everyone is on the same page.

## What's next for Real-Time-Data-Analysis-of-Social-Media-Posts

1. Let customers know how people are feeling about jetblue in real time while booking tickets.
2. Immediately send alert messages to the jetblue staff in real time and respond accordingly.
3. Search for tweets in all languages.
## Inspiration

Our inspiration was to create an easier customer experience with JetBlue Airlines and to give customers the power to give feedback and make change.

## What it does

Allows customers to give feedback in an efficient way. Allows the to contact customer service with out having to search for them. and to follow JetBlue on social media and stay up to date with what's going on

## How we built it

We started by using Appy-pie to provide us with a baseline of how we wanted to build our app. Then we created 50 mock feedback submissions to represent real customer feedback. From there we gathered the data and organized it in excel so it was cohesive and easy to read. After putting the data together in excel, We started with python. In python, we created a text blob with the user feedback. We then used \_ word\_count \_ to retrieve five most used words throughout the users feedback. We also looked at the satisfaction ratings from the app and used \_ word\_counts \_ again to find what user were rating their experience as. Then we created a bar graphs to show our data.

## Challenges we ran into

We had trouble figuring out how to prove a correlation between the satisfaction rating and its cause.

## Accomplishments that we're proud of

Our Teammate made her first app

## What we learned

We learned appy-pie. And more about the \_ pandas \_ package in python

## What's next for JetBlue Feedback App

Being able to handle larger data sets and making the app more efficient.
This code allows the user to take photos of animals, and the app determines whether the photos are pleasing enough for people to see the cuteness of the animals.