# Improving RE-SWOT Analysis with Sentiment Classification: A Case Study of Travel Agencies
## 5 Discussion, Implications, and Conclusions
### Limitations and Future Work  
The limitations of this study may be considered future research directions. First, following [PERSON] and [PERSON]'s method, the extracted features were fine-grained and mentioned as word pairs. When there are more competitors to compare, the number of reviews becomes large, and the comparison of fine-grained features becomes challenging and time-consuming [46]. Therefore, [PERSON] et al. suggested that the comparison should be performed on high-level features. A so-called high-level feature represents the main function and characteristic of a product. [PERSON] et al. used the weather forecasting app as an example. If three different word pairs extracted from user reviews are \"Terrible accuracy\", \"Inaccurate Forecast\", and \"Excellent accuracy\", these three fine-grained features present the same high-level feature: \"Forecast accuracy\". Obviously, the use of high-level features can reduce the workload in regard to comparisons. Therefore, we may employ a high-level feature extraction model in future work. Second, some researchers have found that combining SWOT analysis with another strategic planning framework, such as the PESTEL (political, economic, sociological, technological, environmental, and legal) framework, AHP (analytic hierarchy process), or five forces model, can provide more accurate information and allow a more powerful strategy to be formed [32; 33]. In future research, we may study how to add another strategic planning framework into our procedure and propose a hybrid RE-SWOT tool. Third, some of the settings in this study were inherited from [PERSON] and [PERSON]'s method. For example, this study used five-point scale ratings, and the parameter \(\sigma\) was set at 0.1. We predict that different settings would lead to different results. It may be worth trying a different threshold to quantize the ratings or using a greater number of quantization levels. Additionally, we could use a different \(\sigma\) value and observe the results. Through such exploratory experiments, we may identify a rule of thumb for these settings. Fourth, other than lexicon and rule-based sentiment analysis tools, such as VADER, there are tools that are implemented in machine-learning-based sentiment analysis. VADER is easy to use, but it may be worth trying other sentiment analysis techniques and observing the results. Different sentiment analysis techniques perform differently in terms of accuracy, time complexity, and other metrics [24]. It is worth noting that some metrics are trade-offs. In addition, the performance of sentiment analysis technology may also be affected by the dataset. Fifth, this study focused on English user reviews, but user reviews in different languages would also provide valuable information for OTAs, because tourists can come from all around the world. However, existing NLP tools do not support non-English languages sufficiently. Although there are more than 7000 languages in the world, most NLP processes use seven major languages: English, Chinese, Urdu, Persian, Arabic, French, and Spanish [57]. Design of a non-English NLP tool is beyond the scope of this study. It is expected that NLP tools will support more languages in the future, so we will be able to extend our method to other languages. Sixth, this research mainly focuses on the use of sentiment analysis to solve the problem of RE-SWOT, and less attention is paid to the research topics of sentiment analysis itself. There are some common and important issues in sentiment analysis, such as how to deal with opposite polarities of sentiment in the same review. This study relied on VADER for sentiment analysis, and no additional algorithm is designed to deal with such situation. This may affect the validity of the research results, and further research on this topic should be considered in the future. Finally, a performance evaluation of the results achieved in this paper needs the help of domain experts in a rigorous process and is a topic for future work.