Popular sites such as Twitter and Facebook and other channels are now filled with pictures that help a person better express thoughts and feelings. New research suggests “big data” — any collection of data sets so large or complex that it is difficult to process using traditional data processing applications — can be used to teach computers to interpret the content and feelings associated with images.
Dr. Jiebo Luo, professor of computer science at the University of Rochester, in collaboration with researchers at Adobe Research recently presented a paper at an American Association for Artificial Intelligence (AAAI) conference, describing a progressive training deep convolutional neural network (CNN).
The trained computer can then be used to determine what sentiments these images are likely to elicit. Luo says that this information could be useful for things as diverse as measuring economic indicators or predicting elections.
The task is complex, however. Sentiment analysis of text by computers is itself a challenging task. And in social media, sentiment analysis is more complicated because many people express themselves using images and videos, which are more difficult for a computer to understand.
For example, during a political campaign voters will often share their views through pictures.
Two different pictures might show the same candidate, but they might be making very different political statements. A human could recognize one as being a positive portrait of the candidate (e.g. the candidate smiling and raising his arms) and the other one being negative (e.g. a picture of the candidate looking defeated).
But no human could look at every picture shared on social media — it is truly “big data.” To be able to make informed guesses about a candidate’s popularity, computers need to be trained to digest this data, which is what Luo and his collaborators’ approach can do more accurately than was possible until now.
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