[Objective] This paper builds a spatial-textual sentiment analyzing model based on multi-dimensional WaveCluster, aiming to analyze text sentiment and spatial position effectively. [Methods] First, we integrated several datasets from Yelp to build spatial-textual database. Then, we used lexicon-based sentiment analysis to generate feature vector. Third, we proposed a new method using Hybrid model, Textual-Spatial model, as well as multi-dimensional clustering model to analyze the data. [Results] We found that multi-dimensional clustering based on db2 or bior2.2 wavelet can recognize clusters more accurately than DBSCAN and K-means on spatial-textual feature mining. It also achieved the highest speed for data at 100 thousand to 10 million levels. [Limitations] We used unigram model for sentiment analysis, which cannot analyze sentences. [Conclusions] The proposed Textual-Spatial model could find out sentiment tendency distribution from spatial-textual data effectively. The Hybrid model provides a new approach for spatial-textual recommend system to calculate sentiment similarity and spatial proximity simultaneously.
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