MVSA: Sentiment Analysis on Multi-view Social Data

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.


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..

 Positive: Positive
 Neutral:  Neutral
 Negative  Negative

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 (, 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:



SentiBank, Attribute, BoVW, Color Histogram, Gist and LBP:

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.


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},