Title :
Reviews Analysis Based on Sentence and Word Relevance
Author :
Shibo Zhang ; Yun Sha ; Xiaojie Wang
Author_Institution :
Sch. of Comput. Sci. & Technol., Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
Assuming that one sentence in review expresses one opinion, LDA based on sentence is performed to analysis the massive online reviews. When computing the topic´s word, word relevance measure is designed which penalizes the word frequency by a factor that captures how much the word is shared across topics, words for topics can been selected more accurately. Experiments on massive review crawled from network show that the result of analyzing is better than the standard LDA, there is clearer topic cue, and recognition is improved among the topics.
Keywords :
Internet; natural language processing; LDA; latent Dirichlet allocation; online reviews; reviews analysis; sentence; word frequency; word relevance; word relevance measure; Analytical models; Computational modeling; Entropy; Frequency conversion; Frequency measurement; Resource management; Standards; latent dirichlet allocation model; online reviews; topic analysis;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
Print_ISBN :
978-1-4799-7004-9
DOI :
10.1109/ISCID.2014.21