## 1 Introduction and Literature Survey  
### Background  
Since its invention a couple of decades ago, topic modeling-a technique based on co-occurrence data-has routinely been applied to reveal thematic patterns \"hidden\" in diverse and ever-growing collections of texts [1, 2]. Besides text documents, topic models were built to classify image [3], video [4], audio [5], and time-series [6] data, and have become an indispensable tool of business intelligence [7, 8]. In the e-commerce industry, this technique has been used increasingly to analyze various documents accumulated online. For instance, topic modeling was performed on customer reviews to uncover social and behavioral patterns but also individual experiences associated with consumer goods [9, 10], hospitality [11, 12], and tourism [13, 14]. The successful application of topic modeling to solving these and many other semantic classification problems made it a popular approach to assist or even fully automate the data annotation process [15]. Creating a set of labeled data is a common preliminary task when developing machine learning classifier systems. At the same time, however, producing so-called \"gold standard\" (i.e., by human experts) training datasets has long been a bottleneck for obtaining high quality classification models. Expert annotations usually require a significant amount of time to be completed and are thus also expensive. Various cost-saving approaches, such as crowdsourcing, have been proposed. However, this approach turned out to produce discrepant and noisy data, and is time-consuming, too [16]. Furthermore, data annotation relying on human intervention can quickly become infeasible when dealing with a large amount of constantly flowing information [17] that is, however, a typical situation in the e-commerce industry. As a remedy, various machine learning techniques were proposed to produce \"bronze standard\" labeled datasets in an unsupervised or semi-supervised manner, minimizing the human involvement [15]. Probabilistic generative models built through topic modeling were utilized to establish semantic membership and assign labels to images [18], social media streaming data [19; 20], or textual documents [21]. While being theoretically straightforward, topic model-driven annotation is prone to errors when annotated data include multiple topic-specific segments [22]. Relying on a topic distribution to decide a single label may prevent capturing segment-defined topics, especially in the case of short yet seldom-focused texts, such as online reviews [23]. To break up multi-topic documents into semantically coherent, single label sections text segmentation (TS) would be deployed (e.g., see [24; 25]). TS algorithms produce segments (not necessarily topic-bounded) without labels or other semantic information [26]. The latter makes TS alone a poor fit for document automated annotation. While it might appear only natural to try to combine TS with a generative topic model in a context of unsupervised or semi-supervised data annotation, research on the joint application of these two methods remains scarce. The study described in this paper, therefore, proposes a joint, TS-enhanced topic modeling approach to automated annotation, allowing one to deal with documents which potentially belong to multiple topics. A novel TS algorithm called TopicDiff-LDA is developed to identify topic-bounded segments within a text, based on the topic probability distribution generated with LDA. While breaking multi-topic texts into semantically coherent units, the algorithm works to accommodate for the corresponding changes in class-membership assignments and, consequently, to fine-tune the LDA model. As a result, the updated model provides for a more accurate classification of the segments than with baseline unsupervised methods. The latter makes the developed approach a sound solution to annotate and automatically classify customer-generated texts-requests, feedback, product reviews, etc.-accumulated by online e-commerce systems.