## 5 Conclusions
### Practical Implications  
The visual representations produced through the analysis in this study offer an important contribution, as they allow industry managers to easily understand and utilize the data from the models and rule-mining for the purpose of improving their business strategies. For instance, based on the AR model, this study presented a web graph analysis that shows the strength of pair-wise relationships between different complaint attributes. Additionally, when more than two items are employed for rule generation, the ARs are able to depict even more insights and discover frequent item sets [21]. For these reasons, the Apriori algorithm was applied in this study in order to find the most frequent attributes of complaints and uncover hidden predictive information. After joining and pruning, the top 17 Apriori rules for different star-rated hotels were identified. These rules provide segment-specific information about the online complaint behavior of guests, and can be utilized by management to conduct hotel service quality policy analyses. By applying rule mining techniques, researchers and industry managers can develop a broad view of the relationships that exist between different parameters and simplify the processes of interpretation using models that are easier to understand [107].  
Additionally, based on the results of the qualitative projective technique, we suggest the need for more in-depth investigations into applications of machine learning for the purpose of shedding light on online complaining behavior related to hospitality. More information is needed to understand the way in which algorithms can be used to predict customer complaining behavior. These new techniques will be able to provide new insight by overcoming some of the barriers that traditional methods are hindered by.  
In sum, AR models are able to find patterns in the data where one or more entities (such as events, purchases, or attributes) are associated with one or more other entities [24]. The models construct rule sets that define these relationships, and the fields within the data can act as both inputs and targets. While traditional ARs allow for the manual discovery of patterns, AR algorithms (e.g., Apriori) harness technology to execute the work rapidly while allowing for greater insight into more complex patterns. ARs are also incredibly useful for predicting multiple outcomes--for example, if customers complain about attribute X, they will likely complain about Y and then Z as well. They can be used to associate a particular conclusion (such as the decision to complain about something) with a certain set of conditions, and they have an advantage over the more standard decision tree because of their ability to find connections between any of the attributes. The reason for this is that decision trees build rules with only a single conclusion, whereas ARs attempt to find many rules, each of which may have a different conclusion [24]. Therefore, if hotelers don't know which specific issue(s) to improve on, we recommend using ARs to help identify issues while also establishing their interconnected relationships. Depending on time, priorities, and company goals, hotelers may want to place their focus on anywhere between 5 and 10 of the top rules to gain a better understanding of multiple issues at once. However, if they have very specific problems that need to be solved, decision trees are still recommended as they are able to go into greater depth and detail.