In a world of increasing social pressures, Everyone wants to be popular. But popularity is no longer determined by your physical manifestation alone. No, now the spectrum is broader. Whether we like it or not, the online world is as definitive of who you are as the physical one. This begs the question... How do we garner popularity online? The answer: #Hashtags.

What it does

HashTagGenerator creates hashtags based on user input and images. By analyzing the image and the input text itself, the app generates a range of hashtags, from the hilarious to the serious and everything in between. The app also searches twitter for usages of the hashtag, allowing you to chose unique tags that represent your content, or see what's trending on the social media platform.

How we built it

The app is built using Java which runs python scripts using process builder in java. It integrates the twitter api to search for hashtags in twitter, imagga for image tagging/recognition, and imgur to upload our own photos for image processing. We built a swing app to have a nice UI for the java program that reads user input and selects relevant keyword. We then generate random, relevant and mostly hilarious hashtags for the user to use.

Challenges we ran into

A lot of these technologies were very new to us also so we spent a lot of time learning new technologies. Another problem we had was that we were hitting the twitter search limit for our user account and couldn't really figure out how to use an app authentication for more api calls.

Accomplishments that we're proud of

We are proud to say that our hashtag keywords are generated perfectly. Although we got weird suggestions sometimes, it is because we don't have machine learning or anything of that sort.

What we learned

We learned that we needed more preparation going in to a hackathon. We learned a lot about our limitations as a team also, and how to overcome the limitations by approaching in a different angle.

What's next for #HashTagGenerator

Mobile or chrome integration. More twitter api calls. Possibly machine learning for better suggestions.

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