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Extracting textual features from tweets is a challenging process due to thenoisy nature of the content and the weak signal of most of the words used. Inthis paper, we propose using singular value decomposition (SVD) with clusteringto enhance the signals of the textual features in the tweets to improve thecorrelation with events. The proposed technique applies SVD to the time seriesvector for each feature to factorize the matrix of feature/day counts, in orderto ensure the independence of the feature vectors. Afterwards, the k-meansclustering is applied to build a look-up table that maps members of eachcluster to the cluster-centroid. The lookup table is used to map each featurein the original data to the centroid of its cluster, then we calculate the sumof the term frequency vectors of all features in each cluster to theterm-frequency-vector of the cluster centroid. To test the technique wecalculated the correlations of the cluster centroids with the golden standardrecord (GSR) vector before and after summing the vectors of the cluster membersto the centroid-vector. The proposed method is applied to multiple correlationtechniques including the Pearson, Spearman, distance correlation and KendalTao. The experiments have also considered the different word forms and lengthsof the features including keywords, n-grams, skip-grams and bags-of-words. Thecorrelation results are enhanced significantly as the highest correlationscores have increased from 0.3 to 0.6, and the average correlation scores haveincreased from 0.3 to 0.4.

Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study