The invention relates to a sentiment word extension-based short text sentiment classifying method, and belongs to the technical field of computers and
information science. The sentiment word extension-based short text sentiment classifying method comprises the following steps: first, segmenting a comment text into a
sentence set, and dividing words and
labelling parts of speech by utilizing a jieba word dividing tool to obtain a pre-processed result; then, aiming at each short text comment, acquiring the word vector of each word by using Wikipedia corpus training Glove, calculating the
semantic similarity of other words and the primary sentiment features with the parts of speech of N, V, Adj and Adv by utilizing the word vectors, expanding the words with similar
semantics to a primary sentiment
feature set; next, proposing DF-TF-MI, performing
feature dimension reduction by improving a conventional
feature dimension reduction method by utilizing interlexical statistical features to obtain a low-dimension
feature set, and weighting the sentiment features; finally, performing sentiment tendency classification on the obtained feature vectors through an RADA
algorithm formed by weak classifier weighting. According to the sentiment word extension-based short text sentiment classifying method, the problem that unregistered words exit in a sentiment dictionary is solved, meanwhile, the problem of sparse sentiment features caused by few effective sentiment words of the short text comment is solved, and the performance and accuracy of sentiment tendency analysis are improved.