ESAFN

Dataset and codes for our paper "Entity-Sensitive Attention and Fusion Network for Entity-Level Multimodal Sentiment Classification".

Paper:

Entity-Sensitive Attention and Fusion Network for Entity-Level Multimodal Sentiment Classification. Jianfei Yu, Jing Jiang, and Rui Xia. Accepted by IEEE/ACM TASLP 2020.

Author

Jianfei Yu

[email protected]

Dec 2, 2019

Requirement

• PyTorch 1.0.0
• Python 3.7

Download tweet images and set up image path

• Step 1: Download each tweet's associated image via this link (https://drive.google.com/file/d/1PpvvncnQkgDNeBMKVgG2zFYuRhbL873g/view)
• Step 2: Change the image path in line 468 and line 472 of the "train.py" file
• Step 3: Download the pre-trained ResNet-152 via this link (https://download.pytorch.org/models/resnet152-b121ed2d.pth)
• Setp 4: Put the pre-trained ResNet-152 model under the folder named "resnet"
• Setp 5: Change the Glove word embedding path in line 30 of the "data_utils.py" file

Code Usage

Training for ESAFN

• This is the training code of tuning parameters on the dev set, and testing on the test set. Note that you can change "CUDA_VISIBLE_DEVICES=0" based on your available GPUs.

sh run.sh

• To show the running process, we show our running logs in the "twitter_log.txt" and the "twitter2015_log.txt".