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T4SA-2.0

Introduction

This work introduced the T4SA 2.0 dataset, i.e. a big set of data to train visual models for Sentiment Analysis in the Twitter domain.

It was used to fine-tune the Vision-Transformer (ViT) model pre-trained on Imagenet-21k, which was able to achieve incredible results on external benchmarks which were manually annotated, even beating the current State Of The Art!

It was built crawling ∼3.7 pictures from the social media during the continuous period 1 April-30 June and then labeling them using a cross-modal approach. In particular, a distant supervised technique was used to get rid of human annotators, thus minimizing the efforts required and allowing for the creation of a huge dataset.

The latter can help future research to train robust visual models. Its size would be particularly advantageous as the number of parameters of current SOTA is exponentially growing along with their need of data to avoid overfitting problems.

Please, check the Documentation for a complete view.

Build Instructions

Install git

$ apt-get update
$ apt-get install git

Install T4SA codebase

$ git config --global http.postBuffer 1048576000
$ git clone --recursive https://github.com/codiceSpaghetti/T4SA-2.0.git
$ cd T4SA-2.0/

Install dependencies

$ chmod +x install_dependencies.sh
$ ./install_dependencies.sh

How to use script for benchmark evaluation

Test a model with a benchmark, get the accuracy and save the prediction

$ python3 python\ script/test_benchmark.py -m <model_name> -b <benchmark_name>

$ options for <model_name>: [boosted_model, ViT_L16, ViT_L32, ViT_B16, ViT_B32, merged_T4SA, bal_flat_T4SA2.0, bal_T4SA2.0, unb_T4SA2.0, B-T4SA_1.0_upd_filt, B-T4SA_1.0_upd, B-T4SA_1.0]
$ options for <benchmark_name>: [5agree, 4agree, 3agree, FI_complete, emotion_ROI_test, twitter_testing_2]

Execute a five fold cross validation on a benchmark, get the mean accuracy, the standard deviation and save the predictions (by default use the boosted_model)

$ python3 python\ script/5_fold_cross.py -b <benchmark_name>

Fine tune FI on the five split, get the mean accuracy, the standard deviation and save the predictions (by default use the boosted_model)

$ python3 python\ script/fine_tune_FI.py

Directory structure

.
├── colab notebook
├── dataset
│   ├── benchmark
│   │   ├── EmotionROI
│   │   │   └── images
│   │   │       ├── anger
│   │   │       ├── disgust
│   │   │       ├── fear
│   │   │       ├── joy
│   │   │       ├── sadness
│   │   │       └── surprise
│   │   ├── FI
│   │   │   ├── images
│   │   │   │   ├── amusement
│   │   │   │   ├── anger
│   │   │   │   ├── awe
│   │   │   │   ├── contentment
│   │   │   │   ├── disgust
│   │   │   │   ├── excitement
│   │   │   │   ├── fear
│   │   │   │   └── sadness
│   │   │   ├── split_1
│   │   │   ├── split_2
│   │   │   ├── split_3
│   │   │   ├── split_4
│   │   │   └── split_5
│   │   ├── Twitter Testing Dataset I
│   │   │   └── images
│   │   └── Twitter Testing Dataset II
│   │       └── images
│   ├── t4sa 1.0
│   │   ├── dataset with new labels
│   │   │   ├── b-t4sa1.0 updated
│   │   │   └── b-t4sa1.0 updated and filtered
│   │   └── original dataset
│   │       └── b-t4sa 1.0
│   └── t4sa 2.0
│       ├── bal_T4SA2.0
│       ├── bal_flat_T4SA2.0
│       ├── img
│       ├── merged_T4SA
│       └── unb_T4SA2.0
├── models
├── predictions
└── python script