# BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis
## 1 Introduction  
Topic modelling as unsupervised learning has become a prevalent text mining tool for discovery of hidden semantic structures in a text body. Given a collection of documents, most of the existing topic models perform a full analysis to discover _all_ topics occurring in the corpus. However, it was recently noticed [1] that in many situations users are interested in focused topics related to some specific aspects only. For example, given a set of Amazon product reviews, a user might be interested only in bedding products. A conventional topic model performing full analysis will identify all topics from the entire corpus such as \"furniture\", \"food\" and \"clothing\". Although the topic of \"furniture\" is related to the user interested aspect of \"bedding products\", it is too coarse as the user might be more interested in fine-grained topics like \"bed frames\" and \"matrness\". As a result, targeted (or focused) analysis is proposed by [PERSON] et al. [1] to discover topics relevant to targeted aspects only. Particularly, given a corpus of documents from a broad domain and a set of user-provided keywords representing user-interested aspects, targeted analysis aims to discover topics related with the queried aspects only. Methods for targeted analysis can be generally categorised into two groups: (1) conventional topic models incorporating filtering strategies and (2) specialised topic models. However, methods of both categories suffer from problems such as _topic loss_ and _topic suppression_, because of the limitations of their respective assumptions and strategies. For algorithms in the first group, both pre-filtering and post-filtering strategies can be adopted to empower full-analysis topic models to find topics related to queried aspects. Basically, the pre-filtering strategy retains only documents containing the query keywords and extracts topics from the retained \"partial data\". The quality of the discovered topics thus heavily depends on user-supplied query keywords. If the keywords are not appropriate or comprehensive enough, many relevant documents will be filtered, which incurs a significant _topic loss_. For example, if a user provides \"bath\" as a query keyword, documents without the keyword but containing the synonyms like \"shower\" and similar words like \"bathtub\" will be filtered although such documents are actually relevant. Consequently, there is a great possibility to lose topics if modelling from the retained partial data. A post-filtering strategy applies conventional topic models to identify first all topics in the corpus and then filter the topics that do not contain the query keywords in the results. However, as analysed in [1], such a strategy may result in _topic suppression_ when the query keywords are infrequent in the database. _Topic suppression_ means that topics related to the user interested aspect are suppressed by general topics. For algorithms in the second group, TTM [1] is the first and the state-of-the-art. TTM is a sparse topic model designed to directly mine focused topics based on user-provided query keywords. TTM simulates two topic-word distributions: \(\phi^{r}\) for relevant topics and \(\phi^{ir}\) for irrelevant topics. It considers documents at the sentence level and introduces a variable \(r\) to indicate the status of a sentence (e.g., relevant or irrelevant). Words are then sampled from \(\phi^{r}\) or \(\phi^{ir}\) according to the sentence status. Although TTM can accomplish the targeted analysis to a certain extent, the effectiveness of TTM is handicapped by its scheme of processing at the sentence level and its assumption that each sentence focuses on only one topic. By considering sentences individually and separately, topic information between consecutive sentences may be lost, which results in inferior topic qualities and possible topic loss.