## 7 Smart Planning Using ACD  
In this section we discuss the process of augmenting the planning process with the proposed technique in order to make it smarter. Smart planning can be done at individual as well as community level. For example, for the presented case study of restaurant reviews, the user may only consider the reviews classified in the aspect category(ies) and plan on the basis of these reviews. This makes the decision-making process pragmatic and beneficial for the user. Community level planning can be improved by looking at the aspect categories that are marked important by a community. The simplest approach to identify important categories is to look at the most commented aspect categories. Other sophisticated techniques like clustering and customized filtering can also be employed for the identification of important categories. The outcome of this process is fed into the planning process to enable the meaningful participation of the community in the planning process. The work flow diagram of the aforementioned process is shown in Figure 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Category** & **Precision** & **Recall** & **F1 Scores** \\ \hline \(L1\)-\(SOW\) & Normalized & 95.80 & 94.19 & 94.99 \\ \(L1\)-\(DOW\) & Normalized & 95.25 & 93.29 & 94.26 \\ \(L1\)-\(AvgDOW\) & Normalized & 93.85 & 94.58 & 94.21 \\ \(DOW\) & Un-Normalized & 93.14 & 92.90 & 93.02 \\ \(SOW\)\(\oplus DOW\) & Un-Normalized & 89.28 & 91.35 & 90.30 \\ \(SOW\) & Un-Normalized & 90.27 & 89.80 & 90.03 \\ \(L2\)-\(SOW\) & Normalized & 80.33 & 93.80 & 86.54 \\ \(L2\)-\(AvgSOW\) & Normalized & 79.81 & 90.32 & 84.74 \\ \(Avg\)-\(SUM\) & Normalized & 79.16 & 90.19 & 84.31 \\ \(L2\)-\(AvgDOW\) & Normalized & 75.55 & 92.12 & 83.02 \\ \(L2\)-\(DOW\) & Normalized & 75.26 & 90.70 & 82.27 \\ \(L2\)-\(AvgSOW\) & Normalized & 74.65 & 89.67 & 81.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results summary of aspect category detection in SemEval-2015 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Category** & **Precision** & **Recall** & **F1 Scores** \\ \hline \(SOW\) & Un-Normalized & 77.04 & 75.90 & 76.40 \\ \(SOW\)\(\oplus DOW\) & Un-Normalized & 75.09 & 76.31 & 75.70 \\ \(DOW\) & Un-Normalized & 76.26 & 74.83 & 75.54 \\ \(L1\)-\(DOW\) & Normalized & 77.46 & 73.08 & 75.20 \\ \(L1\)-\(SOW\) & Normalized & 77.41 & 72.40 & 74.82 \\ \(L1\)-\(AvgDOW\) & Normalized & 75.20 & 73.88 & 74.54 \\ \(L1\)-\(AvgSOW\) & Normalized & 74.62 & 73.21 & 73.91 \\ \(L2\)-\(SOW\) & Normalized & 69.65 & 78.46 & 73.79 \\ \(L2\)-\(DOW\) & Normalized & 67.88 & 77.65 & 72.44 \\ \(L2\)-\(AvgDOW\) & Normalized & 65.74 & 80.08 & 72.20 \\ \(Avg\)-\(SUM\) & Normalized & 66.66 & 77.79 & 71.80 \\ \(L2\)-\(AvgSOW\) & Normalized & 65.82 & 77.52 & 71.19 \\ \(Avg\)-\(DOW\) & Normalized & 62.88 & 79.81 & 70.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results summary of aspect category detection in SemEval-2016 dataset. For example, for the same case study, smart community level planning might include designing a restaurant that is most beneficial for the general public. For this, a simple count of group-based reviews for each aspect category identifies the most important features expected by the community in a good restaurant. A frequency bar graph for 12 aspect categories for new/unseen reviews is shown in Figure 6.  
After examining this graph, it is easy to infer that all good restaurant plans will include special attention to \"Food Quality\", \"Service\" and \"Ambience\". Interestingly, the community seems to be oblivious to the high food prices if a restaurant is performing well on the aforementioned three categories. This can be beneficial to the planners/restaurant owners to maximize their profits. Figure 5: A simple work flow to show the use of ACD for making the planning process smart and efficient. Figure 6: Importance of different aspects of the reviews calculated after completion of ACD task. Frequency represents the number of times a reviewer has commented.