# BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis
## 4 Experimental Results
### Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{6}{c}{**BiTTM**} \\ \cline{2-7}  & **TopM = 5** & **TopM = 10** & **TopM = 15** & **TopM = 20** & **TopM = 25** & **TopM = 30** \\ \hline cigar & \(-\)43.85160224 & \(-\)213.938738 & \(-\)515.6799558 & \(-\)942.5318026 & \(-\)1493.639111 & \(-\)2170.963155 \\ \hline ecig & \(-\)47.18005764 & \(-\)225.7818028 & \(-\)537.0456006 & \(-\)975.5867746 & \(-\)1540.959263 & \(-\)2233.885475 \\ \hline Oscars & \(-\)41.56838081 & \(-\)203.696938 & \(-\)495.7966598 & \(-\)913.9376539 & \(-\)1451.063314 & \(-\)2102.62643 \\ \hline baby & \(-\)18.77161638 & \(-\)97.92745779 & \(-\)258.704099 & \(-\)512.2443085 & \(-\)862.3028153 & \(-\)1314.514524 \\ \hline camera & \(-\)11.60928852 & \(-\)57.83209167 & \(-\)151.6040027 & \(-\)295.3511581 & \(-\)493.2409214 & \(-\)748.8210303 \\ \hline computer & \(-\)14.82764795 & \(-\)66.09833632 & \(-\)163.5166346 & \(-\)310.8723517 & \(-\)517.6532656 & \(-\)789.6438756 \\ \hline home & \(-\)9.146932399 & \(-\)45.84159746 & \(-\)117.7837843 & \(-\)239.7455224 & \(-\)418.9237371 & \(-\)656.914665 \\ \hline food & \(-\)7.47342957 & \(-\)42.52194503 & \(-\)109.7998671 & \(-\)220.2148208 & \(-\)370.0466113 & \(-\)563.1454128 \\ \hline care & \(-\)11.52868834 & \(-\)49.17233659 & \(-\)116.4456707 & \(-\)224.1156048 & \(-\)375.6415389 & \(-\)585.870988 \\ \hline \multicolumn{7}{c}{**TTM**} \\ \cline{2-7}  & **TopM = 5** & **TopM = 10** & **TopM = 15** & **TopM = 20** & **TopM = 25** & **TopM = 30** \\ \hline cigar & \(-\)34.68247162 & \(-\)197.0478114 & \(-\)493.1432304 & \(-\)921.1591797 & \(-\)1481.235217 & \(-\)2165.174583 \\ \hline ecig & \(-\)34.67967533 & \(-\)200.4202621 & \(-\)502.3021289 & \(-\)935.8301943 & \(-\)1495.777983 & \(-\)2182.933002 \\ \hline Oscars & \(-\)31.17239749 & \(-\)178.8829525 & \(-\)456.1151923 & \(-\)858.0192159 & \(-\)1383.122495 & \(-\)2032.967068 \\ \hline baby & \(-\)16.2223621 & \(-\)101.2752879 & \(-\)281.5019366 & \(-\)562.4756204 & \(-\)937.8116571 & \(-\)1440.487996 \\ \hline camera & \(-\)11.28057698 & \(-\)62.01834755 & \(-\)164.0824617 & \(-\)318.5467601 & \(-\)529.0200582 & \(-\)801.4709905 \\ \hline computer & \(-\)13.16215736 & \(-\)68.04826101 & \(-\)174.2534566 & \(-\)332.9993329 & \(-\)549.2408003 & \(-\)824.3810116 \\ \hline home & \(-\)9.934943893 & \(-\)51.89310174 & \(-\)131.4846734 & \(-\)254.4960003 & \(-\)428.6019462 & \(-\)643.0140473 \\ \hline food & \(-\)11.33222981 & \(-\)54.36687511 & \(-\)128.2609949 & \(-\)242.0789656 & \(-\)399.9506445 & \(-\)600.8773534 \\ \hline care & \(-\)9.739145131 & \(-\)53.9780039 & \(-\)133.7955495 & \(-\)254.3544155 & \(-\)432.013784 & \(-\)666.1259446 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Topic coherence. Figure 4: Effect of various targets on topic coherence. The \(x\)-axis shows the word frequency of targets, and the y-axis represents the percentage of documents containing a target. contrary, BiTTM achieves satisfactory performance for a diverse range of targets even if they are infrequent in the corpus. This also verifies the effectiveness of using core words to enrich the semantic information in the context of the target keyword (i.e., BiTTM strategy) than taking words in same sentences as bridges to connect potentially target-related words (i.e., TTM strategy). **Analysing the results of \(P@n\):** To calculate the measure of \(P@n\), similar to TTM, three human labelers familiar with the data sets are engaged to label the results. The \(P@n\) values at the rank positions of 5, 10 and 20 are reported in Table 5, from which several interesting outcomes can be observed. Firstly, the performance of the two variations of BTM (i.e., BTM-PD and BTM*) is generally worse than that of the two specialised topic models (i.e., BiTTM and TTM), which demonstrates that full-analysis topic models with filtering strategies are not suitable for targeted analysis because they are prone to detect general topics instead of fine-grained target-related topics.