## 6 Conclusions and Future Work  
This paper describes the architecture of a real-world web application whose objective is to identify news reports that are related to traffic incidents, extract their locations, and display them on a map. To this end, it was necessary combinedly applying NLP techniques and machine learning algorithms.  
This paper contributes to the literature with a corpus of 18,389 RSS news reports, establishing a standard benchmark for further research on NLP in Spanish. This corpus was made up of news reports from an RSS endpoint provided by the Diario de Juarez, a local newspaper in Mexico, and it is composed of two classes that were manually assigned: 'traffic accident' and 'not traffic accident'. The class of interest is 'traffic accident', which is the minority one.  
Five classification algorithms were assessed: CART, Complement Naive Bayes, \(k\)NN, Random Forest, and SVM. Before the training process, the corpus was preprocessed by the consecutive application of the bag-of-words technique, TF-IDF normalization, and feature selection based on mutual information. We found SVM and Random Forest as the most skillful classifiers after conducting computational experiments.  
By the obtained results, we concluded that the imbalance between both classes negatively affected the performance of both classifiers. Therefore, sampling algorithms were applied to increase their performance in terms of sensitivity and PR-AUC. A comparative analysis was made to select a proper pair of algorithms--classifier and sampler--for addressing this case study. The best results were obtained when random oversampling and SVM were applied together.  
In this way, SVM improved the recall 29% (on average) by identifying more patterns belonging to the minority class, without degrading the values of PR-AUC and ROC-AUC (indeed, they were slightly enhanced). This paper empirically evidences the impact of sampling algorithms on the classifiers under conditions of class imbalance and high dimensionality. Random oversampling obtained the most encouraging results among the sampling algorithms tested. Although a side effect of resampling is to diminish precision, NLP techniques based on grammar patterns could mitigate this drawback when they are applied jointly to Machine Learning approaches; in our experiments, the location extraction module was able to identify--indirectly--the 71% of the false-positive cases.  
As future work, we are going to explore other ways to represent the RSS news reports. We mean implementing schemata with more capacities than the bag of words, which allow capturing semantic information (e.g., [26; 28; 29]). Interestingly, the most elementary sampling algorithms--ROS and RUS--proved to be the most effective to address this case study, additional calculations of the other methods did not make any contribution; ergo, we are going to investigate the properties of this corpus (other than its high dimensionality when it is vectorized) to identify the causes and factors behind such behavior; this knowledge would be convenient to face other real-world problems of this nature.  
All authors contributed significantly in writing this paper in the following manner: conceptualization, G.R.; and methodology, R.F. and V.G.; formal analysis, V.G. and P.S.-S; software development, R.F.; writing--original draft preparation, G.R., R.F. and A.R.; writing--review and editing, G.R., R.F. and A.R.; validation, V.G.; data curation, J.P.S-S.; visualization, A.R.; investigation J.P.S.-S. and V.G.; supervision, G.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors gratefully acknowledge the support of the EUREKA Community and the Universidad Autonoma de Ciudad Juarez (Division Multidisciplinaria de Ciudad Universitaria).  
The authors declare no conflict of interest.