Short text dataset for classification and clustering extracted from StackOverflow

Note that:

1. If you use this short text dataset, please cite our paper:
   [1]. 2015NAACL VSM-NLP workshop-"Short Text Clustering via Convolutional Neural Networks"
   and acknowledge Kaggle for making the datasets available.
2. We do not remove any stop words or symbols in the text;
3. If you run the Classification ACC.m, please run it on 64-bit machine;
4. Classification is fast, while Clustering is very slow via KMeans on so high-dimensionality text features, about 2 hours once. If you want to run clustering via KMeans, please have a little patience, and we strongly suggest that you directly refer the KMeans results in our paper [1] which reports the average results by running KMeans 500 times;
5. The demo code can be found at https://github.com/jacoxu/STC2
6. Please feel free to send me emails (jacoxu@msn.com) if you have any problems in using this package.

./rawText: Raw text, 20,000 titles as short texts
　　-- label_StackOverflow.txt: Each title plus a tag/label at the end;
　　-- title_StackOverflow.txt: Each title on each line;
　　-- vocab_emb_Word2vec_48.vec: Word2vec trained from a large corpus of StackOverflow dataset;
　　-- vocab_emb_Word2vec_48_index.dic: Word2vec index list corresponds with vocab_withIdx.dic;
　　-- vocab_withIdx.dic: Vocabulary index.

./matlab_format: Matlab format of rawText
　　-- StackOverflow.mat: fea is vsm model, and gnd is the label index.

./benchmarks: Contains some benchmarks, such as classfication and clustering
　　-- Classification_ACC.m: Test the classification performance with TF-IDF+SVM, and the ACC is 81.55%
　　-- predict.mexw64: LibSVM libraries;
　　-- svmpredict.mexw64
　　-- svmtrain.mexw64
　　-- train.mexw64
　　-- tf_idf.m: Compute TF-IDF;
　　-- Clustering_ACC_NMI.m: Test the clustering performance with TF-IDF+KMeans, and the ACC is 20.31% and NMI is 15.64% by 500 runs;
　　-- normalize.m: normalize the feature vectors;
　　-- bestMap.m: Permutation mapping function maps each cluster label to the equivalent label from the text data;
　　-- MutualInfo.m: Compute normalized mutual information metric;

20 different labels:
　　1 wordpress
　　2 oracle
　　3 svn
　　4 apache
　　5 excel
　　6 matlab
　　7 visual-studio
　　8 cocoa
　　9 osx
　　10 bash
　　11 spring
　　12 hibernate
　　13 scala
　　14 sharepoint
　　15 ajax
　　16 qt
　　17 drupal
　　18 linq
　　19 haskell
　　20 magento