Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media
Table 3: Evaluation results with different preprocessing techniques
['[BOLD] Data set  [BOLD] Method', 'MUNLIV  [BOLD] Recall', 'MUNLIV  [BOLD] Precision', 'MUNLIV  [BOLD] F1', 'BrexitVote  [BOLD] Recall', 'BrexitVote  [BOLD] Precision', 'BrexitVote  [BOLD] F1']
Even though there is an improvement in the performance measures with preprocessing, these results show that we can obtain good measures without preprocessing also. This ability will be helpful in situations where we cannot integrate direct preprocessing mechanisms such as removing stop words in a less commonly used language and removing stop words in a data set which is composed by more than one language. Since both data sets used for this research are mainly written in English, we used the tokens without punctuation and stop-words for the following experiments.