emoji classification python

How to reposition emojis in Python?
The python realization is shown below (using the emoji package): Concatenate emojis (concat-emoji) Essentially, we reposition the emojis to the end of the sentence and perform directly encode method.
What are the subfolders of GitHub emoji?
It contains two subfolders: GitHub_data/ contains the processed emoji-texts used to train SEntiMoji. benchmark_dataset/ contains the benchmark datasets used for evaluation. Benchmark dataset includes datasets for sentiment analysis task and emotion detection task.
What is GitHub emoji dataset?
GitHub_data/ contains the processed emoji-texts used to train SEntiMoji. benchmark_dataset/ contains the benchmark datasets used for evaluation. Benchmark dataset includes datasets for sentiment analysis task and emotion detection task. Datasets for sentiment analysis: the Jira, Stack Overflow, Code Review, and Java Library datset.
How to classify facial images with different expressions in Python?
Building an image classification model which can classify facial images with different expressions on them. Extracting the face from an image and then classifying the expression on it using the classifier. Python libraries make it very easy for us to handle the data and perform typical and complex tasks with a single line of code.

Overview

This repository contains the data, code, pre-trained models and experiment results for the paper: [SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering] . github.com

SEntiMoji

This study proposes SEntiMoji, which leverages the texts containing emoji from both Github and Twitter to improve the sentiment analysis and emotion detection task in software engineering (SE) domain. SEntiMoji is demonstrated to be able to significantly outperform the exisiting SE-customized sentiment analysis and emotion detection methods on repr

Running SEntiMoji

1.We assume that you're using Python 3.6 with pip installed. As a backend you need to install either Theano (version 0.9+) or Tensorflow (version 1.3+). For the installation of depedencies, open the command line and run: pip install -r requirements.txt 2.In order to train a sentiment classifer or emotion detector based on SEntiMoji (or the variants of SEntiMoji) model, you can run the scripts in the code/SEntiMoji_script directory. •Train model on provided benchmark datasets. •For sentiment classification task, you have to specify the pretrained model name, task and dataset name in command line. For example, if you want to train and evaluate the classifier on the Jira dataset using the SEntiMoji representation model, just run:python pipeline.py --model SEntiMoji --task sentiment --benchmark_dataset_name Jira. •For emotion detection task, you have to specify the pretrained model name, task, dataset name and emotion type in command line. For example, if you want to train and evaluate the classifier on the Jira LOVE dataset using the SEntiMoji representation model, just run: python pipeline.py --model SEntiMoji --task emotion --benchmark_dataset_name Jira --emotion_type love. •Train model on your own dataset. github.com

Declaration

1.We upload all the benchmark datasets to this repository for convenience. As they were not generated and released by us, we do not claim any rights on them. If you use any of them, please make sure you fulfill the licenses that they were released with and consider citing the original papers. The scripts of baseline methods (SentiStrength, SentiStrength-SE, SentiCR, Senti4SD, EmoTxt, DEVA) are not included in this repository. You can turn to their homepage for downloading. 2.The large-scale Tweets used to train DeepMoji are not released by Felbo et al. due to licensing restrictions. Therefore, we include the pre-trained DeepMoji released rather than the raw Tweet corpus in this repository. github.com

License

This code and the pretrained model is licensed under the MIT license (https://mit-license.org). github.com

Citation

Please consider citing the following paper when using our code or pretrained models for your application. github.com

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