Inspiration
Psychology finds the mere exposure effect as a predetermined deposition on the human mind only due to our past interactions. It is similar to how we become so tolerant to the temperature. We in our minds set this as our sole basis for "normal" and we assume the accepted tolerance and the environment in which it was produced in. A better example of this phenomenon is the repeated song tendencies for radio stations; and marketing analyst have realized that the more played a song is,whether good or bad to our bias taste, we shall remember it and most likely will associate thoughts in which are positive and pleasant
As we further dive in this train of thought, our experiment comes down to whether or not we can succeed in creating a similar " set of opinions" in our A.I. The question only becomes a matter answered through observation and how it will effect our understanding of the subject. Once examined and studied in detail, not only on yourself but on the A.I. we can only guess and try to adjust to the situations in which CASI operates.
What it does
Our program analyzes data that one would be typically exposed to in everyday life. From this analysis, CASI's mood changes based on the difference of the current situation from the average of past historic data.
How we built it
Using Java in the Eclipse Photon IDE. We uitilzed JavaFX for graphics and the google cloud platform along with microsoft azure for text analytics and speech to text conversion.
Challenges we ran into
Many errors when it came to getting API's to work and creating the "mood" algorithm's initial structure. We also had little sleep.
Accomplishments that we're proud of
Finishing the project in 36 hours.
What we learned
We learned about psychology and how to better implement API's into Java using Maven.
What's next for CASI (Computer Automated Sentiment Intelligence)
Further development and this awesome website (http://www.sassycasi.com/)
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