It can be very difficult and expensive to find what a certain target market will be interested in, as well as change their ads accordingly. Our group decided to focus on popular social media site Reddit, to try and see what is most popular at the moment. This program can be much cheaper to a company than other market research, as it benefits off social media that people already use.

What it does

The code will find a sentiment score for specific objects in images to predict which objects have the most positive impact on a photo. This can be used to extrapolate which objects should be included in images to result in a high performing post for marketing strategies.

How we built it

The program begins by scraping Reddit comments and assigns each post a sentiment value for the comments. After, it will assign the sentiment value for the associated comments to each object in the image. Then it will repeat this for many images and comments and get an average for each of the detected objects, the detection of objects is done using the IBM Watson API.The sentiment of each object is graphed, to see which object has the largest positive impact. We used another algorithm that suggests users what they can add to their own photo in order to improve the positivity based on the sentiment from other objects. It was built on Python and uses Indico API as well as Watson IBM for the analysis.

Challenges we ran into

Both of the APIs had their difficulties when used in a batch situation. We were often kicked out of the Indico API when processing large batches of information and due to the depth of the complexity of the Indico API, the sentimental analysis often took a long time. We would have liked to create some filtering criteria, in order to make this process more efficient if we had more time.

Accomplishments that we're proud of

The program was successfully build on time, and works quite well

What we learned

We learned how to incorporate APIs into python and furthered our knowledge in how to use python as well as introduced us to the field of data science.

What's next for RedditSentimenter

We plan on using data bases to save the results of sentiment value for each object, after each run of the program in order to make consistently improving suggestions, and adding filters to make the data cleaner and more relevant. Theres also potential to create our own neural network that learns sentiment of objects by scraping multiple sources on the internet. The program will receive a GUI to make it easier to use as well as be deployable to many computers.

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