Mams For Absa
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Twitter Sentiment Analysis | 1,322 | 7 months ago | 20 | mit | Python | |||||
| Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc. | ||||||||||
| Nsc | 280 | 5 years ago | 6 | mit | Python | |||||
| Neural Sentiment Classification | ||||||||||
| Mmsa | 278 | a year ago | 11 | January 25, 2022 | 9 | mit | Python | |||
| MMSA is a unified framework for Multimodal Sentiment Analysis. | ||||||||||
| Nsmc | 259 | 7 years ago | 1 | Python | ||||||
| Naver sentiment movie corpus | ||||||||||
| E2e Tbsa | 222 | 2 years ago | 7 | Python | ||||||
| A Unified Model for Opinion Target Extraction and Target Sentiment Prediction (AAAI 2019) | ||||||||||
| Linusrants | 208 | 3 years ago | Python | |||||||
| Dataset of Linus Torvalds' rants classified by negativity using sentiment analysis | ||||||||||
| Chinese_conversation_sentiment | 190 | 7 years ago | 4 | |||||||
| A Chinese sentiment dataset may be useful for sentiment analysis. | ||||||||||
| Sentiment | 189 | 3 years ago | May 24, 2021 | 4 | mit | Go | ||||
| Simple Sentiment Analysis in Golang | ||||||||||
| Sentiment And Style Transfer | 183 | 4 years ago | 8 | Roff | ||||||
| Twitter Sentiment Cnn | 133 | 6 years ago | 5 | Python | ||||||
| An implementation in TensorFlow of a convolutional neural network (CNN) to perform sentiment classification on tweets. | ||||||||||
MAMS-for-ABSA
This repository contains the data and code for the paper "A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis", EMNLP-IJCNLP 2019, [paper].
MAMS
MAMS is a challenge dataset for aspect-based sentiment analysis (ABSA), in which each sentences contain at least two aspects with different sentiment polarities. MAMS dataset contains two versions: one for aspect-term sentiment analysis (ATSA) and one for aspect-category sentiment analysis (ACSA).
Requirements
pytorch==1.1.0
spacy==2.1.8
pytorch-pretrained-bert==0.6.2
adabound==0.0.5
pyyaml==5.1.2
numpy==1.17.2
scikit-learn==0.21.3
scipy==1.3.1
Quick Start
Put the pretrained GloVe(http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip) file glove.840B.300d.txt in folder ./data.
Modify config.py to select task, model and hyper-parameters. When mode is set to term, base_path should point to an ATSA dataset. When mode is set to category, base_path should point to an ACSA dataset.
Preprocessing
python preprocess.py
Train
python train.py
Test
python test.py
Acknowledgement
The BERT model pretrained by huggingface(huggingface/pytorch-transformers) is used in our experiments.
