Sentiment Analysis is a sentence-classification task. It may be targeting binary classes(Positive-Negative) or graded classes(VP-Positive-Neutral-Negative-VN, etc.). VictorNLP-Sentiment framework supplies various deep learning sentiment analysis models. In-depth description of resources and their implementation details are recorded right here:

VictorNLP_Sentiment Documentation

VictorNLP, a PyTorch-based NLP framework, provides intuitive interfaces and carefully designed code modularity for implementing and pipelining NLP modules. For more details, refer to the VictorNLP Specification page.

VictorNLP Specification

1. Clone the repository to your local computer.

git clone https://github.com/jinulee-v/victornlp_sentiment

2. Install victornlp_sentiment package using pip.

cd victornlp_sentiment
mkdir corpus model
pip install -e ./victornlp_sentiment

Note: All commands in this documentation should be executed in the same directory.

ls

corpus model victornlp_dp

3. Install dependencies.

pip install -r victornlp_dp/requirements.txt
apt-get install default-jre # For KOMORAN PoS tagger
cd ..

Refer to VictorNLP_Utils documentation.

Add downloaded files to victornlp_dp/victornlp_utils/data as instructed.

If the script raises NotFoundError, precautious warnings containing missing dependencies are printed.

This script automatically reads config_file and starts training. However, for convenience, overriding configuration file with command line arguments is supported. However, to reduce command line bizarreness, we only support major hyper-parameters. Details like model hyperparameters(num_layers, dropout, ...) are likely to be modified directly in victornlp_dp/config.json or other config files.

python -m victornlp_sentiment.train

Refer to python -m victornlp_sentiment.train -h for more information, and train.sh for some demos.

This script also supports fine-tuning for different possible situations:

• Re-train with another corpus.
• Fine-tuning of pre-trained embeddings. Refer to the following code snippet or train-finetune.sh for examples. You can override any cmd line argument-supported hyperparameter just as training, and you can also modify models/Example_name/config.json for more precise and specific tuning.

python -m victornlp_sentiment.train --finetune-model models/Example_name

ls models/Example

• model.pt
• config.json

Use this script to test model stats or parse sentences of interest. You only require --model-dir argument which is a directory name that contains model.pt and config.json. These two files are automatically generated by the victornlp_sentiment.train script. Command line arguments can modify the script's behavior from testing to parsing. Details are given below.

python victornlp_sentiment.test --model-dir ./model/(folder name)

Optional Command line arguments:

• -a, --analyze : Can freely select which analyzing functions(victornlp_sentiment/tools/analyze.py) to apply. Test set requires golden labels
• --data-file : path/to/input/file. Load VictorNLP_format .json file to parse. If not provided, parses stdin inputs(line by line, PoS automatically tagged).
• --save-result : path/to/store/parsed_file.json. Stores parsed result in the designated path.