# NSC

**Repository Path**: mirrors_thunlp/NSC

## Basic Information

- **Project Name**: NSC
- **Description**: Neural Sentiment Classification
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-11
- **Last Updated**: 2025-12-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Neural Sentiment Classification
==========

Neural Sentiment Classification aims to classify the sentiment in a document with neural models, which has been the state-of-the-art methods for sentiment classification. In this project, we provide our implementations of NSC, NSC+LA and NSC+UPA [Chen et al., 2016] in which user and product information is considered via attentions over different semantic levels.

Evaluation Results
==========

Evaluation results on document-level sentiment classification. Acc.(Accuracy) and RMSE are the evaluation metrics.
![image](https://github.com/huimchen/figure/blob/master/evaluation%20results.png)

In the above table, baseline models including Majority, Trigram, TextFeature, UPF, AvgWordvec, SSWE, RNTN + RNN, Paragraph Vector, JMARS and UPNN are reported in [Tang et al., 2015].

Data
==========

We provide IMDB, Yelp13 and Yelp14 datasets we used for sentiment classification in [[Download]](http://www.thunlp.org/~chm/data/data.zip). The dataset should be decompressed and put in the folder NSC/, NSC+LA/ or NSC+UPA/.

We prepocess the original data to make it satisfy the input format of our codes. The original datasets are released by the paper [Tang et al., 2015]. [[Download]](http://ir.hit.edu.cn/~dytang/paper/acl2015/dataset.7z)

Pre-trained word vectors are learned on each dataset (IMDB, Yelp13, Yelp14) separately.

The dataset in each domain contains seven files, using the following format:
+ train.txt: training file, format (userid	productid	class	document), split by '\t'.
+ dev.txt: dev file, same format as train.txt.
+ test.txt: test file, same format as train.txt.
+ wordlist.txt: corresponding words with same sequence in pre-trained word vectors, one per line.
+ usrlist.txt: user ids in each dataset, per one line.
+ prdlist.txt: product ids in each dataset, per one line.
+ embinit.save: the pre-trained word embedding file, which is saved as pickle and can be loaded from pickle to numpy arrays.

The trained model can be found at this [link](http://www.thunlp.org/~chm/data/emnlp2016_model.zip).

Codes
==========

The source codes of various models are put in the folders NSC/src, NSC+LA/src, NSC+UPA/src.


Train
==========

For training, you need to type the following command in the folder src/ of each model:

	THEANO_FLAGS="floatX=float32,device=gpu" python train.py $dataset $class

where *dataset* is the corresponding dataset folder, *class* is the number of corresponding domain.

For example, we use the following command when classfing the IMDB document:

	THEANO_FLAGS="floatX=float32,device=gpu" python train.py IMDB 10

The training model file will be saved in the folder model/bestmodel/ of each model.

Test
==========

For testing, you need to type the following command in the folder src/ of each model:

	THEANO_FLAGS="floatX=float32,device=gpu" python test.py $dataset $class

where *dataset* is the corresponding dataset folder, *class* is the number of corresponding domain.

For example, we use the following command when classfing the IMDB document:

	THEANO_FLAGS="floatX=float32,device=gpu" python test.py IMDB 10

The testing result which reports the Accuracy and RMSE will be shown in screen.

Cite
==========

If you use the code, please cite the following paper:

[Chen et al., 2016] Huimin Chen, Maosong Sun, Cunchao Tu, Yankai Lin and Zhiyuan Liu. Neural Sentiment Classification with User and Product Attention. In proceedings of EMNLP.[[pdf]](http://www.thunlp.org/~chm/publications/emnlp2016_NSCUPA.pdf)

Reference
==========
[Chen et al., 2016] Huimin Chen, Maosong Sun, Cunchao Tu, Yankai Lin and Zhiyuan Liu. Neural Sentiment Classification with User and Product Attention. In proceedings of EMNLP.[[pdf]](http://www.thunlp.org/~chm/publications/emnlp2016_NSCUPA.pdf)

[Tang et al., 2015] Duyu Tang, Bing Qin, Ting Liu. Learning Semantic Representations of Users and Products for Document Level Sentiment Classification. In Proceedings of EMNLP.