Name
Abstract | ||
|---|---|---|
In this proposal, we study the problem of understandinghuman sentiments from large scale collection ofInternet images based on both image features and contextualsocial network information (such as friend comments anduser description). Despite the great strides in analyzing usersentiment based on text information, the analysis of sentimentbehind the image content has largely been ignored. Thus, we extend the significant advances in text-based sentimentprediction tasks to the higherlevel challenge of predicting theunderlying sentiments behind the images. We show that neithervisual features nor the textual features are by themselvessufficient for accurate sentiment labeling. Thus, we provide away of using both of them, and formulate sentiment predictionproblem in two scenarios: supervised and unsupervised. Wedevelop an optimization algorithm for finding a local-optimasolution under the proposed framework. With experiments ontwo large-scale datasets, we show that the proposed methodimproves significantly over existing state-of-the-art methods. In the future, we are going to incorporating more informationon the social network and explore sentiment on signed social network. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDMW.2015.142 | ICDM Workshops |
Field | DocType | Citations |
Data mining,Social network,Computer science,Image content,Artificial intelligence,Optimization algorithm,Social media,Information retrieval,Feature (computer vision),Visualization,Sentiment analysis,Feature extraction,Machine learning | Conference | 8 |
PageRank | References | Authors |
0.43 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yilin Wang | 1 | 163 | 9.77 |
| Baoxin Li | 2 | 1017 | 94.72 |