  Extracting textual features from tweets is a challenging process due to the
noisy nature of the content and the weak signal of most of the words used. In
this paper, we propose using singular value decomposition (SVD) with clustering
to enhance the signals of the textual features in the tweets to improve the
correlation with events. The proposed technique applies SVD to the time series
vector for each feature to factorize the matrix of feature/day counts, in order
to ensure the independence of the feature vectors. Afterwards, the k-means
clustering is applied to build a look-up table that maps members of each
cluster to the cluster-centroid. The lookup table is used to map each feature
in the original data to the centroid of its cluster, then we calculate the sum
of the term frequency vectors of all features in each cluster to the
term-frequency-vector of the cluster centroid. To test the technique we
calculated the correlations of the cluster centroids with the golden standard
record (GSR) vector before and after summing the vectors of the cluster members
to the centroid-vector. The proposed method is applied to multiple correlation
techniques including the Pearson, Spearman, distance correlation and Kendal
Tao. The experiments have also considered the different word forms and lengths
of the features including keywords, n-grams, skip-grams and bags-of-words. The
correlation results are enhanced significantly as the highest correlation
scores have increased from 0.3 to 0.6, and the average correlation scores have
increased from 0.3 to 0.4.
