# BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis
## 1 Introduction
By assuming that each sentence is related with only one aspect, it is very likely for TTM to mistakenly assign relevance status for sentences related with multiple topics, which is often the case for long sentences. The wrong assignment of sentence statuses will in turn lead to possible missing of meaningful topics. A common challenge faced by algorithms of both categories is the computation efficiency, while full analysis of topics is largely performed offline, targeted analysis is more likely an online module that is supposed to respond to user queries as soon as possible. However, existing algorithms for targeted analysis, especially the post-filtering strategy and the specialised topic models, are not devised to address this issue. The pre-filtering strategy may gain efficiency by modelling topics from a reduced set of \"partial data\", but it achieves this at the cost of losing important topics. To address the aforementioned issues, we propose a novel _Core BiTerm-based Topic Model_ (BiTTM) for targeted analysis, which directly models fine-grained topics related to the queried aspect from a set of _core biterms_. Biterm, proposed in BTM [2], is a word-pair consisting of two different words that appear together in a fixed-size window and represent co-occurrence information. Improving biterms, we introduce _core biterms_ as a set of selected biterms that have strong connections with query keywords. By modelling topics from the set of core biterms, BiTTM is expected to achieve better performance than existing specialised topic models in terms of the following aspects:  
1. The existing specialised topic models for targeted analysis (i.e., TTM and APSUM [3]) process at either the sentence level or the word level so that the semantic information between consecutive sentences will be lost. In contrast, since a biterm may consist of two words coming from two successive sentences, information across the whole document can be captured by BiTTM to alleviate the issue of losing topics. 2. The TTM model samples relevance status at the sentence level which may be too coarse. When a sentence is related to multiple topics, it would be difficult to infer the relevance status of the sentence as a binary value. In contrast, the APSUM model [3] samples relevance status for individual words which may be too specific, because it cannot handle phrases that make sense when multiple words are considered together. Biterms, as a scheme in-between sentences and words, are expected to achieve more accurate inference of relevance status. 3. Existing specialised topic models do not have any finesse to accelerate the calculation without significant semantic information losing. Instead, BiTTM introduces a heuristic preprocessing based on core biterms for speeding topic modelling while alleviating information loss, which makes it a more pragmatic solution for targeted analysis according to user queries. To comprehensively evaluate the performance of BiTTM, extensive experiments have been conducted on real-world datasets including short texts, medium texts and long texts. Moreover, we select a large number of targets with different word and document frequencies to explore the adaptability of BiTTM to various types of queries. The experimental results show that (1) BiTTM improves the quality of topics, alleviates topic losing, and outperforms baselines especially for query keywords of low frequencies; (2) the time cost of BiTTM is most outstanding and stable compared to those of the baselines, which demonstrates the high applicability of BiTTM on datasets with different characteristics. The remainder of this paper is organised as follows. Prior research and related works are reviewed in Section 2. We provide technical details of BiTTM in Section 3, and discuss the experimental results in Section 4. Finally, Section 5 closes this paper with some conclusive remarks.