Emotion Text Classification

Summary

Introduction

Emotion text classification can be approached using various techniques in natural language processing (NLP), including rule-based methods, machine learning algorithms, and deep learning models. These approaches aim to extract features from the text and train models to classify the text into 6 different emotion categories: sadness, joy, love, anger, fear, surprise.

Parameters

Inputs

input - (text): The textual information provided by the user needs to be analyzed to determine the type of emotion.

Output

output - (text): Includes the model's predictions for each label, displayed as probabilities, with 6 types of emotion labels.

Examples

input	output
Ugh! I have to beat this stupid song to get to the next rude!

Usage for developers

Please find below the details to track the information and access the code for processing the model on our platform.

Requirements

torch
transformers

Code based on AIOZ structure

import torch, os
from transformers import pipeline

...
def do_ai_task(
        input: Union[str, Path],
        model_storage_directory: Union[str, Path],
        device: Literal["cpu", "cuda", "gpu"] = "cpu",
        *args, **kwargs) -> Any:
    """Define AI task: load model, pre-process, post-process, etc ..."""
    # Define AI task workflow. Below is an example
    device = "cuda" if torch.cuda.is_available() else "cpu"

    path_weight = os.path.abspath(model_storage_directory + "/...") 
    with torch.no_grad():
        model = pipeline("text-classification", model=path_weight, device= device)

    label = model(input)[0]["label"]
    return str(label)

Reference

This repository is based on and inspired by Muntasir Hossain's work, which is a fine-tuned version of roberta-based on the emotion dataset. We sincerely appreciate their generosity in sharing the code.

License

We respect and comply with the terms of the author's license cited in the Reference section.

Citation

@article{DBLP:journals/corr/abs-1907-11692,
  author    = {Yinhan Liu and
               Myle Ott and
               Naman Goyal and
               Jingfei Du and
               Mandar Joshi and
               Danqi Chen and
               Omer Levy and
               Mike Lewis and
               Luke Zettlemoyer and
               Veselin Stoyanov},
  title     = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
  journal   = {CoRR},
  volume    = {abs/1907.11692},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.11692},
  archivePrefix = {arXiv},
  eprint    = {1907.11692},
  timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}