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Emotion_and_Polarity_SO

(★ 76)

An emotion classifier of text containing technical content from the SE domain

파일 탐색기

최종 버전 다운로드 (.zip)

- - SentiStrengthCom.jar
- Emotion_and_Polarity_SO.jar
- Emotion_and_Polarity_SO_old.jar
- pom.xml
- - moodAndModality.py
- - __init__.py
  - LICENSE
  - model.py
  - politeness-svm.p
  - politeness.pdf
  - README.md
  - request_utils.py
  - requirements.txt
- - classification.R
  - modelsLiblinear.txt
  - svmLiblinearDownSampling.R
  - svmLiblinearWithoutDownSampling.R
.gitattributes
.gitignore
classify.sh
LOVE.csv
LOVE_SMALL.csv
README.md
requirements.R
requirements.txt
sample.csv
train.sh

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/collab-uniba/Emotion_and_Polarity_SO

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd Emotion_and_Polarity_SO

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Maven (Java)

보통 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
JDK (Java) Java 프로젝트를 빌드/실행하려면 필요합니다.
Maven mvn 명령어를 쓰기 위한 빌드 도구입니다.

mvn clean install

Maven으로 의존성 설치와 빌드를 진행합니다.

✅ BUILD SUCCESS 메시지가 뜨면 성공입니다. target/ 폴더에 결과물이 생성됩니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

`$ pip install -r requirements.txt`

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# EmoTxt
A toolkit for emotion detection from technical text. It is part of the Collab Emotion Mining Toolkit ([EMTk](https://github.com/collab-uniba/EMTk)).

## Fair Use Policy
Please, cite the following paper if you intend to use our tool for your own research:
> F. Calefato, F. Lanubile, N. Novielli. “[EmoTxt: A Toolkit for Emotion Recognition from Text](https://arxiv.org/abs/1708.03892)” In *Proceedings of the Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, {ACII} Workshops 2017*, San Antonio, USA, Oct. 23-26, 2017, pp. 79-80, ISBN: 978-1-5386-0563-9.

## Installation
You will need to install [Git LFS](https://git-lfs.github.com) extension to check out this project. Once installed and initialized, simply run:

```bash
$ git clone https://github.com/collab-uniba/Emotion_and_Polarity_SO.git
```

### Requirements
* Ram: 8GB 
* Python 2.7.x
  * Libraries
    * `nltk`, `numpy`, `scikit_learn`, `scipy`, `pattern`
      * Installation: open the command line and run
      `$ pip install -r requirements.txt`
      
      * Complete the nltk installation: Run the Python interpreter and type the commands
        ```
        >>> import nltk        
        >>> nltk.download()
        ```

* Java 8+
  * Maven 3.x
    * if you want to build the jar yourself type the following commands
      ```bash
      cd java
      mvn clean install
      ```
    * The fat jar will be generated in the `java/target`folder with the name `EmotionAndPolarity-0.0.1-SNAPSHOT-jar-with-dependencies.jar`. Rename it as `Emotion_and_Polarity_SO.jar` and move it directly under the `java` folder.

* R
  * Libraries:
    * `caret`, `LiblinearR` , `e1071`
      * Installation: open the command line and run
      
        `$ Rscript requirements.R`

## Usage and dataset
In the following, we show first how to train a new model for emotion classification and, then, how to test the model on unseen data.

For testing purposes, you can use the `sample.csv` input file available in the root of the repo. Other, more complex examples, look at the dataset files available under the subfolder [./java/DatasetSO/StackOverflowCSV](https://github.com/collab-uniba/Emotion_and_Polarity_SO/tree/master/java/DatasetSO/StackOverflowCSV).

If you are looking for the entire experimental dataset of ~5K Stack Overflow posts annotated with emotion, it is available from [this repository](https://github.com/collab-uniba/EmotionDatasetMSR18).

### Training a new model for emotion classification (70/30% split for train and test)
```bash
$ sh train.sh -i file.csv -d delimiter [-g] [-p] -e emotion 
```

where:

* `-i file.csv`: the input file coded in **UTF-8 without BOM**, containing the input corpus. Please, note that gold label are required for each item in the dataset. The format of the input file is the following:

```
  id;label;text
  ...
  22;NO;"""Excellent! This is exactly what I needed. Thanks!"""
  23;YES;"""FEAR!!!!!!!!!!!"""
  ...
  ```
* `-d delimiter`: the delimiter used in the csv file (values in {`c`, `sc`}, where stands for comma and sc for semicolon). Please, note that all the example files provided here use semicolon as delimiter, so `-d sc` is a mandatory option during tests.
* `-g`: enables the extraction of n-grams (i.e,. bigrams and unigrams). N-grams extraction is mandatory for the first run when you want to train a new classification model for a given emotion, using your own dataset for the first time. Because n-gram extraction is computationally expensive, it should be skipped if you retrain the model for the same emotion using the same input file.
* `-p`: enables the extraction of features regarding politeness, mood and modality. Because this is computationally expensive, the switch is off by default.
* `-e emotion`: the specific emotion for which you want to train a classification model, with values in {`joy`, `anger`, `sadness`, `love`, `surprise`, `fear`}.

As a result, the script will generate the following output files:

* An output folder named `training_<file.csv>_<emotion>/`, containing:
   * `n-grams/`: a subfolder containing the extracted n-grams
   * `idfs/`: a subfolder containing the IDFs computed for n-grams and WordNet Affect emotion words
   * `feature-<emotion>.csv`: a .csv file with the features extracted from the input corpus and used for training the model
   * `liblinear/`:
     * there are two subfolders: `DownSampling/` and `NoDownSampling/`. Each one contains:
          * `trainingSet.csv`
          * `testingSet.csv`
          * eight models trained with liblinear `model_<emotion>_<IDMODEL>.Rda`, where `IDMODEL` is the ID of the liblinear model, with values in `{0,...,7}`):
          * `performance_<emotion>_<IDMODEL>.txt`, containing the results of the parameter tuning for the model (best C) as performed by caret, the confusion matrix and the precision, recall and f-measure for the best cost for the specific emotion
          * `predictions_<emotion>_<IDMODEL>.csv`, containing the test instances with the predicted labels for the specific emotion

### Emotion detection
```bash
$ sh classify.sh -i file.csv -d delimiter -e emotion [-m model] [-f idf] [-o n-grams] [-l] [-p]
```

where:

* `-i file.csv`: the input csv file with header and coded in **UTF-8 without BOM**, containing the corpus to be classified; the format of the input file is the following:

```
  id;label;text
  ...
  22;NO;"""Excellent! This is exactly what I needed. Thanks!"""
  23;YES;"""FEAR!!!!!!!!!!!"""
  ...
  ```
* `-d delimiter`: the delimiter used in the csv file (values in {`c`, `sc`}, where stands for comma and sc for semicolon). Please, note that all the example files provided here use semicolon as delimiter, so `-d sc` is a mandatory option during tests.
* `-e emotion`: the specific emotion to be detected in the input file or text, defined in {`joy`, `anger`, `sadness`, `love`, `surprise`, `fear`}.
* `-m model`: the model file learnt during the training step (e.g., `model-anger.rda`). If you don't specify the model name, the default model will be used, that is the one learnt on our Stack Overflow gold standard.
* `-o n-grams`: if you specify a model name using `-m` (i.e., you don't want to use the default model for a given emotion) you are required to provide also the path to the folder containing the dictionaries extracted during the training step. This folder includes n-grams, i.e., `UnigramsList.txt` and `BigramsList.txt`. 
* `-f idf`: if you specify a model name using `-m` (i.e., you don't want to use the default model for a given emotion) you are required to specify also the path to the folder containing the dictionaries with IDFs computed during the training step. The folder includes IDFs for n-grams (uni- and bi-grams) and for WordNet Affect lists of emotion words.
* `-l`: if presents , indicates  `<file.csv>` contains a gold label in the column `label`.
* `-p`: enables the extraction of features regarding politeness, mood and modality. Because this is computationally expensive, the switch is off by default.

As a result, the script will create an output folder named `classification_<file.csv>_<emotion>` containing:

* `predictions_<emotion>.csv`: a csv file with header, containing a binary prediction (yes/no) for each line of the input corpus:

```
  id;predicted
  ...
  22;NO
  23;YES
  ...
  ```
* `performance_<emotion>.txt`: a file created only if the input corpus `<file.csv>` contains the column `label`; the file contains several performance metrics (Precision, Recall, F1, confusion matrix).

For example, if you wanted to detect *anger* in the input file `sample.csv`, you would have to run:
```bash
$ sh classify.sh -i sample.csv -d sc -p -e anger
```