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https://github.com/thunlp/nsc

Neural Sentiment Classification
https://github.com/thunlp/nsc

nlp

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Neural Sentiment Classification

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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

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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

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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

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



Train

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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

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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.