Clustering is a powerful unsupervised tool for sentiment analysis from text. However, the clustering results may be affected by any step of the clustering process, such as data pre-processing strategy, term weighting method in Vector Space Model and clustering algorithm. This paper presents the results of an experimental study of some common clustering techniques with respect to the task of sentiment analysis. Different from previous studies, in particular, we investigate the combination effects of these factors with a series of comprehensive experimental studies. The experimental results indicate that, first, the K-means-type clustering algorithms show clear advantages on balanced review datasets, while performing rather poorly on unbalanced datasets by considering clustering accuracy. Second, the comparatively newly designed weighting models are better than the traditional weighting models for sentiment clustering on both balanced and unbalanced datasets. Furthermore, adjective and adverb words extraction strategy can offer obvious improvements on clustering performance, while strategies of adopting stemming and stopword removal will bring negative influences on sentiment clustering. The experimental results would be valuable for both the study and usage of clustering methods in online review sentiment analysis.

In this work, we propose a novel method to summarize popular information from massive tourism blog data. First, we crawl blog contents and segment them into semantic word vectors separately. Then, we select the geographical terms in each word vector into a corresponding geographical term vector and present a new method to explore hot tourism locations and, in particular, their frequent sequential relations from a set of geographical term vectors. Third, we propose a novel word vector subdividing method to collect local features for each hot location, and introduce the metric of max-confidence to identify the Things of Interest (ToI) associated with the location from the collected data. We illustrate the benefits of this approach by applying it to a Chinese online tourism blog dataset. The experimental results show that the proposed method can be used to explore hot locations, as well as their sequential relations and corresponding ToI, efficiently.