# BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis
## 2 Related Work
### Targeted Topic Models  
Specialised topic models for targeted analysis are still rarely seen, which are mainly used for information retrieval [4], abstract extraction [3; 5] and opinion mining [1; 6]. TTM [1] and APSUM [3] are the two most representative models.  
[PERSON] et al. first study the problem to detect relevant and user-concerned topics from a given dataset [1] and propose the model TTM as illustrated in Figure 1. The main idea of TTM is to introduce a relevance variable \(r\) to indicate whether a sentence is related with a specified aspect. The variable \(r\) determines whether each word in a sentence is generated by a related topic or an irrelevant topic. Moreover, the relevant topic-word distribution \(\varphi^{r}\) is sparse because the number of words related to the target is usually less than that of the irrelevant words.  
The steps of generative process are illustrated as follows:  
1. Draw \(\varphi^{ir}\sim Dirichlet(\beta^{ir})\)  
Figure 1: The graphical model of TTM.  
For each relevant topic \(k\in\{1,2,\cdots,T\}\)  
1. Draw \({}_{k}\sim Beta(p,q)\).
2. For each word \(v\in\{1,2,\cdots,V\}\) i.  Draw \(\beta^{\prime}_{t,v}\sim Bernoulli(\omega_{t})\).
3. Draw \(\varphi^{\prime}_{t}\sim Dirichlet(\beta^{\prime}_{t}\delta+\epsilon)\).
3. For each document \(m\in\{1,2,\cdots,M\}\) 1. Draw \(\pi_{m}\sim Beta(\gamma)\).
4. raw relevance status \(r\) based on keyword indicator \(x\) and \(Bernoulli(\pi_{m})\).
5. If the document is relevant i.  Draw \(z\sim Multinomial(\theta^{r})\). 2. Draw \(w_{i}\sim Multinomial(\varphi^{r}_{z})\) 4. If the document is irrelevant i.  Draw \(w_{i}\sim Multinomial(\varphi^{ir})\).  
Therefore, TTM considers the status \(r\) at the sentence-level. It is difficult to determine whether a sentence is related to the target when a sentence contains multiple topics. The wrong assignment of sentence status will negatively affect the quality of topics.  
APSUM [3] is a generative aspect summarisation model designed for fine-grained summaries of online reviews. Compared with TTM, APSUM is different in terms of the following two aspects. Firstly, while TTM models the relevance at the sentence level, APSUM considers at the word level. As discussed in Section 1, the former might be too coarse to determine the relevance status for sentences appropriately; the latter is not able to handle phrases where it makes sense only when multiple words are considered together. Secondly, APSUM introduces an additional component called _document aggregator_ to mitigate the issue of aspect sparsity, which refers to the circumstances where there are not enough text data related with specific aspects. The basic idea is to cluster similar documents through document aggregator and sample topics for documents at the document aggregator level.  
Essentially, both TTM and APSUM try to identify potentially related words that can serve as bridges to link relevant documents, especially those without containing query keywords. TTM searches such related words from sentences containing query keywords; APSUM attempts from semantically similar documents through document aggregator. However, both models fail to capture the semantic information between neighbouring sentences, which does exist in natural language [7].