<![CDATA[Animation videos are growing significantly producing tens even hundreds of titles per year. Certainly not everything were produced was interesting. Some of these videos may not be appealing to some people. To find out whether the animated videos is interesting or not, users can read the reviews given by other user about animation videos. Some sites that are intended to facilitate its users to be able give each other feed back about the animation video they have watched. From those reviews can be seen sentiment whether the review is a review that classified in to positive class sentiment or negative class sentiment. The Latent Semantic Indexing (LSI) method that adopts the Singular Value Decomposition (SVD) matrix reduction process is used to find the relevance between documents. With the LSI method helps us to be able to know the reviews are classified on positive sentiment or negative sentiment. The TF IDF method is used to process textual data into numerical data and cosine similiarity method is used to calculate the similiarity between data which is further classified into positive class sentiment and negative class sentiment. Testing done as much as 19 times by using different k-rank input. Based on the test result, this system produces an optimal accuracy on k-rank =10 that is equal to 86% so we can conclude that latent semantic indexing is good to use for searching relevance between documents.]]>
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