New: We are pleased to release our new MVSA dataset including more tweets and annotations. In the new dataset, each tweet is annotated by three annotators. We name this dataset as MVSA-multiple. The original MVSA used in [1], where each tweet only has one label, is named as MVSA-single.
Overview
There is an increasing interest in understanding users’ attitude or sentiment towards a specific topic (e.g., a brand) from the large repository of opinion-rich data on the Web. While great efforts have been devoted on the single media, either text or image, little attempts are paid for the joint analysis of multi-view data which is becoming a prevalent form in the social media. To prompt the research on this interesting and important problem, we introduce a multi-view sentiment analysis dataset (MVSA) including a set of image-text pairs with manual annotations collected from Twitter. The dataset can be utilized as a valuable benchmark for both single-view and multi-view sentiment analysis..
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The Dataset
MVSA-multiple can be downloaded from MVSA-multiple on One Drive and MVSA-multiple on BaiduYun.
MVSA-single can be downloaded from MVSA- single on One Drive and MVSA- single on BaiduYun .
We provide following information:
- Original image-text pairs collected from Twitter.
- Annotation for both text and image.
Please contact Dr. Shiai Zhu (zshiai@gmail.com), if any problems on our dataset.
Pipeline for sentiment analysis
We adopt the standard statistical learning methods for single-view and multi-view sentiment analysis.
Some useful links for extracting visual features including low-level to middle-level features are as follows:
Classemes: http://vlg.cs.dartmouth.edu/projects/vlg_extractor/vlg_extractor/Home.html
Aesthetic:
http://www.ee.columbia.edu/~subh/Software.php
SentiBank, Attribute, BoVW, Color Histogram, Gist and LBP:
http://www.ee.columbia.edu/ln/dvmm/vso/download/sentibank.html
Please cite our paper if the datasets are helpful to your research:
[1] T. Niu, S. A. Zhu, L. Pang and A. El Saddik, Sentiment Analysis on Multi-view Social Data, MultiMedia Modeling (MMM), pp: 15-27, Miami, 2016.
@inproceedings{MVSA,
author = {Teng Niu and Shiai Zhu and Lei Pang and Abdulmotaleb El{-}Saddik},
title = {Sentiment Analysis on Multi-View Social Data},
booktitle = {MultiMedia Modeling},
pages = {15–27},
year = {2016},
}