# BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis
## 4 Experimental Results
### Qualitative Evaluation
#### 4.5.1 Discovering Relevant Topics  
We take the query \"disease\" in the dataset _food_ as an example. Table 7 shows the topics discovered by the four comparative models, together with the top 10 words of each topic. The third row of Table 7 are the labels we assign manually to summarise the semantics of each topic, where _SFA_ is the abbreviation for _Saturated Fatty Acid_. Words that do not semantically align with the topics are displayed in red. Compared with the topics discovered by BiTTM, all the other three methods fail to identify the topic _prevention_, which is clearly a relevant topic of \"disease\". Moreover, the two BTM variation models (i.e., BTM-PD and BTM\({}^{\star}\)) miss out the topic _risk_. By taking a closer look, we find that this is because the two BTM models cannot distinguish between the two topics _risk_ and _research_ that are different delicately. In other words, the two BTM models discover a topic combining _research_ and _risk_. This is understandable because BTM as a full-analysis topic model discovers general topics. TTM succeeds in discovering both _research_ and _risk_, but the topic quality is poorer than that of BiTTM (e.g., there are more bold words in the two topics discovered by TTM, which means more inconsistent words in results of TTM). Therefore, BiTTM discovers more relevant and fine-grained topics than other models for this example query. Consider the topic _SFA_ that is discovered by all of the four models. Results of BiTTM clearly indicate that saturated fatty acids affect blood sugar and carcinogenesis, but the results of other methods are not satisfactory. For example, TTM tends to find out which foods (e.g., tart, chip and sweetener) have unsaturated fatty acids. BTM-PD and BTM\({}^{\star}\) focus on food ingredients (e.g., palm oil and protein). These results are not related with the target \"disease\" queried by users. Hence, the topic quality of BiTTM is better than that of other models as well in this example. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**BiTTM**} & \multicolumn{4}{c}{**TTM**} & \multicolumn{4}{c}{**BTM-PD**} & \multicolumn{4}{c}{**BTM\({}^{\star}\)**} \\ \hline
**Tea** & **SFA** & **Research** & **Risk** & **Prevention** & **Tea** & **SFA** & **Research** & **Risk** & **Tea** & **SFA** & **Research** & **Tea** & **SFA** & **Research** \\ \hline tea & fat & study & disease & **chery** & disease & fat & heart & risk & tea & **protein** & **chery** & tea & fat & study \\ effect & cancer & increase & risk & reduce & tea & saturated & study & disease & green & **oil** & ** chocolate** & antioxidant & saturated & disease \\ benefit & people & research & heart & **tart** & flavour & **tart** & cancer & **chery** & weight & fat & **tart** & disease & disease & risk \\ work & health & **small** & high & prevent & bad & chip & reduce & star & fat & palm & study & heart & risk & heart \\ lower & **fruit** & antioxidant & green & **include** & **price** & **sweetener** & **order** & **bag** & loss & saturated & disease & **fruit** & oil & reduce \\ taste & blood & **good** & **cell** & body & higher & **follow** & **brand** & **measure** & study & **quality** & **dark** & rich & **economic** & **show** \\
**long** & sugar & **eat** & level & **find** & **company** & **thing** & **vegelable** & calorie & increase & **eat** & **occoa** & **provide** & **find** & **chery** \\ kind & **animal** & **amount** & **show** & product & **back** & production & **cook** & state & body & **diet** & **sweet** & health & **study** & health \\
**pure** & saturated & diet & **day** & result & nutrient & add & **large** & price & drink & disease & cancer & **substitute** & increase & food \\ rich & **drink** & **water** & **sweet** & add & **simply** & **expensive** & **drink** & **shipping** & calorie & **gram** & product & **vegelable** & health & cancer \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The contents of topics on target “disease” in long texts _food_.