# BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis
## 4 Experimental Results
### Time Efficiency Analysis  
As mentioned before, it is ideal for targeted analysis to provide responses to user queries as soon as possible. Therefore, in this experiment, we analyse the time efficiency of the comparative models. The average time cost of the four methods on each dataset over 40 random queries is shown in Table 6. It can be observed that, generally, BiTTM has the best time efficiency, followed by BTM-PD. TTM is significantly slower without any preprocessing strategy, and BTM\({}^{\star}\) is the most inefficient model since BTM performs full analysis on the complete dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Domain** & **Size (KB)** & **BiTTM** & **BTM-PD** & **TTM** & **BTM\({}^{\star}\)** \\ \hline cigar & 641 & 6.012 & **0.378** & 60.994 & 314.940 \\ \hline ecig & 708 & 2.960 & **2.900** & 107.008 & 489.180 \\ \hline Oscars & 565 & **3.620** & 3.934 & 58.042 & 418.560 \\ \hline baby & 141 & 16.011 & **9.753** & 24.246 & 1153.740 \\ \hline camera & 1285 & **81.086** & 164.834 & 811.684 & 15,997.380 \\ \hline computer & 1295 & **96.978** & 147.023 & 713.068 & 17,559.660 \\ \hline home & 619 & **37.083** & 90.283 & 129.481 & 7116.300 \\ \hline food & 195 & **9.166** & 31.833 & 18.534 & 2496.600 \\ \hline care & 1523 & **135.040** & 218.065 & 1516.927 & 18,767.580 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The time cost (seconds) of all datasets. The best performance on each dataset is in bold. To clearly demonstrate the impact of data size on the time efficiency, we plot the results in Figure 5 where the grey bars denote the size of datasets and the polylines in different colours indicate the time cost of different methods. Note that, since the time consumption of BTM* is not comparable to the others, only three models (i.e., BiTTM, BTM-PD and TTM) are displayed in the figure. It can be observed that, generally, the time cost of all methods increases with respect to the increment of data size. However, the size of dataset has a greater impact on TTM than the other two methods, which shows that TTM is not suitable for processing large data sets. In contrast, BiTTM and BTM-PD have a better capability to adapt to large data sets. For these two methods, the difference of data size does not make dramatic changes to time consumption since they both have preprocessing strategies to focus on only the portion of data related to query targets. The difference between BiTTM and BTM-PD is that BiTTM is faster than BTM-PD especially when the length of documents increases. The reason is that the pre-filtering strategy adopted by BTM-PD is a simple and rough processing. It selects documents as long as they contain the query keywords. Consequently, irrelevant information contained by such documents will be included and processed as well, which negatively contributes to the time efficiency of BTM-PD. To illustrate the impact of document length on the time efficiency, the percentage histogram of time cost of BiTTM, TTM and BTM-PD is plotted in Figure 6, where the average document length increases from left to right. It can be observed that the time efficiency of TTM is worst on short texts. Recall that the topic coherence of TTM on short texts is better than BiTTM. This experiment shows that TTM achieves this by significantly sacrificing time efficiency, while the topic quality in terms _P@n_ of TTM on short texts is also worse than that of BiTTM. Moreover, we can see that efficiency performance of BTM-PD is worse on long texts, compared to its performance on short texts. This is because BTM-PD is a biterm-based topic model and long texts generally have more biterms than short texts. Although BiTTM is also a biterm-based model, the strategy of selecting \"core biterms\" removes a lot of irrelevant biterms so that the performance of BiTTM on long texts is also promising.