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Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN
Speaker (s):
TRUONG Quoc Tuan
PhD Student
School of Information Systems
Singapore Management University
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Date:
Time:
Venue:
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October 19, 2017, Thursday
2:00pm - 2:30pm
Meeting Room 4.4, Level 4
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902
We look forward to seeing you at this research seminar.
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About the Talk
Online reviews are prevalent. When recounting their experience with a product, service, or venue, in addition to textual narration, a reviewer frequently includes images as photographic record. While textual sentiment analysis has been widely studied, in this paper we are interested in visual sentiment analysis to infer whether a given image included as part of a review expresses the overall positive or negative sentiment of that review. Visual sentiment analysis can be formulated as image classification using deep learning methods such as Convolutional Neural Networks or CNN. However, we observe that the sentiment captured within an image may be affected by three factors: image factor, user factor, and item factor. Essentially, only the first factor had been taken into account by previous works on visual sentiment analysis. We develop item-oriented and user-oriented CNN that we hypothesize would better capture the interaction of image features with specific expressions of users or items. Experiments on images from restaurant reviews show these to be more effective at classifying the sentiments of review images.
This is a pre-conference talk for 25th ACM Multimedia (ACMMM 2017).
About the Speaker
TRUONG Quoc Tuan is a PhD student advised by Assistant Professor Hady W. Lauw. He received his B.Eng. in University of Engineering and Technology (UET), Vietnam National University, Hanoi (VNU) in 2016. His research interests are in machine learning and data mining area. He has been working on sentiment analysis and recommender systems.
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