Topic models are becoming a frequently employed tool in the empirical methods repertoire of information systems and management scholars. Given textual corpora, such as consumer reviews and online discussion forums, researchers and business practitioners often use topic modeling to either explore data in an unsupervised fashion or generate variables of interest for subsequent econometric analysis. However, one important concern stems from the fact that topic models can be notorious for their instability, i.e., the generated results could be inconsistent and irreproducible at different times, even on the same dataset. Therefore, researchers might arrive at potentially unreliable results regarding the theoretical relationships that they are testing or developing. In this paper, we attempt to highlight this problem and suggest a potential approach to addressing it. First, we empirically define and evaluate the stability problem of topic models using four textual datasets. Next, to alleviate the problem and with the goal of extracting actionable insights from textual data, we propose a new method, Stable LDA, which incorporates topical word clusters into the topic model to steer the model inference toward consistent results. We show that the proposed Stable LDA approach can significantly improve model stability while maintaining or even improving the topic model quality. Further, employing two case studies related to an online knowledge community and online consumer reviews, we demonstrate that the variables generated from Stable LDA can lead to more consistent estimations in econometric analyses. We believe that our work can further enhance management scholars’ collective toolkit to analyze ever-growing textual data.

在文本分类中,如何运用word2vec词向量高效地表达一篇文档一直是一个难点。目前,将word2vec模型与聚类算法结合形成的doc2vec模型能有效地表达文档信息。但是,这种方法很少考虑单个词对整篇文档的影响力。为了解决这个问题,利用TF-IDF算法计算每篇文档中词的权重,并结合word2vec词向量生成文档向量,最后将其应用于中文文档分类。在搜狗中文语料库上的实验验证了新方法的有效性。

In text classification issues,it is difficult to express a document efficiently by the word vector of word2vec.At present,doc2vec built on the combination of word2vec and clustering algorithm can express the information of document very well.However,this method rarely considers a single word’s influence for the entire document.To solve this pro-blem,in this paper, TF-IDF algorithm was used to calculate the right weight of words in documents,and word2vec was combined to generate document vectors,which were used for Chinese text classification.Experiments on the Sogou Chinese corpus laboratory demonstrate the efficiency of this newly proposed algorithm.

K-均值（K-Means）算法是聚类分析中一种基于划分的算法，同时也是无监督学习算法。其具有思想简单、效果好和容易实现的优点，广泛应用于机器学习等领域。但是K-Means算法也有一定的局限性，比如：算法中聚类数目K值难以确定，初始聚类中心如何选取，离群点的检测与去除，距离和相似性度量等。从多个方面对K-Means算法的改进措施进行概括，并和传统K-Means算法进行比较，分析了改进算法的优缺点，指出了其中存在的问题。对K-Means算法的发展方向和趋势进行了展望。

The K-Means algorithm is a partition-based algorithm in cluster analysis. With an unsupervised learning algorithm, its advantages of simple thinking, good effect and easy implementation are widely used in fields such as machine learning. But the K-Means algorithm also has certain limitations. For example, the K number of clusters in the algorithm is difficult to determine how to choose the initial cluster center, how to detect and remove outliers and the distance and similarity measure. This paper summarizes the improvement of K-Means algorithm from several aspects, and compares it with the classical K-Means algorithm. In addition, it analyzes the advantages and disadvantages of the improved algorithm, and points out the problems. Finally, the development direction and trend of K-Means algorithm are prospected.