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SeqCSG

This repository contains code for:

Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph

Model Architecture

<img src="figures/SeqCSG.png" width = 100% height = 100% />

Dependencies

Python 3
PyTorch >= 1.7.1
Transformers>= 4.19.2
NumPy
All experiments are performed with one RTX 3090Ti GPU.

Prerequisites

DATASET: Download the Twitter2015 and Twitter2017 dataset followed instructions in the TomBERT repo, and place them in ./data/.
Cache: We use Scene-Graph-Benchmark to parse scene graphs from the images, then place imageid2triple.json and sub_images in ./cache/.

Code Structures

There are four parts in the code.

model: It contains the main files for SeqCSG network.
data: It contains the data splits for different datasets.
cache: It contains some cache files.
script: The training scripts for SeqCSG.

Train & Eval

The training script for Twitter2015:

bash scripts/run_2015.sh

Parameter

[--dataset {twitter2015, twitter2017}] [--EPOCHS epoch] [--BATCH_SIZE batch_size] [--RANDOM_SEEDS seeds]
[--LEARNING_RATE learning_rate] [--image_feature image_encoder] [--triple_number numbers]

Note:

you can open the .sh file for <a href="#Parameter">parameter</a> modification.