SENTIMENTAL CLASSIFICATION BASED ON KERNEL METHODS AND DOMAIN SEMANTIC ORIENTATION DICTIONARIES
Changqin Quan, Fuji Ren, Tingting He · International journal of innovative computing, information & control · 2010
Kernel methods make use of the document information encoded in the innerproduct between all pairs of document items, avoiding explicitly the computation of the feature vector for a given input, therefore they get considerable attention in classification tasks. In this paper, we focus our attention on the problem of sentimental classification based on three kernel methods: latent semantic kernel (LSK), polynomial kernel (PK), and Gaussian kernel (GK). It is well known that LSK has good performance in text classification, but it has relative low efficiency because of the process of the SVD decomposition, especially runs on large corpora. Our experiments demonstrate that PK has higher precision and efficiency compared with LSK and GK for the problem of sentimental classification. In particular, we compare the performances on different semantic orientation dictionaries, and find that the domain semantic orientation dictionaries can enhance the performance greatly. Also, our method can categorize the reviews with different degrees, such as 5-star, 4-star, . . . and 1-star by sorting the similarities between the reviews and the semantic orientation dictionaries. In our method, tagged corpus and certain rules are not necessary, so it is practical and has high efficiency.