Sentiment analysis based on grid clustering

To expand a lexicon, the methods of point mutual information (PMI), setting the threshold parameter, etc. were used to automatically identify, extract and classification the words which are not included in the HowNet but have a certain emotional tendency. On that basis, a feature vector model based on commodity comments was established, and the SCG (sentiment classification based on grid clustering) algorithm was presented. Next, the grid-based clustering algorithm was used to build up a classification model. The amount of calculation decreased after the dynamic attenuation factors were introduced and sparse grids were periodically removed in the grid-based clustering process. Experimental results indicate that the classification accuracy and field adaptability of SCG is higher, compared with other algorithms such as Naive Bayes, SMO (sequential minimal optimization).