Table 5 Summary of strength and weaknesses of event detection techniques and their attributes
From: Real-time event detection in social media streams through semantic analysis of noisy terms
Unsupervised learning approaches | Semi-supervised learning approaches | Supervised learning approaches | Semantic-based approaches |
|---|---|---|---|
Strength •Detecting events without any particular regard to their nature •Can handle a large volume of data in real-time Weakness •Difficult in dealing with a high dimensionality data stream •It does not consider spatial relationships in the data | •Strength •Particularly useful when it is difficult to extract relevant features from data •Small amount of data can lead to a significant accuracy improvement Weakness •Iteration results are not stable •Low accuracy | Strength •Results are highly accurate and trustworthy Weakness •Time-consuming •Large amount of data to be trained •Handling concept drift •Labels for input and output variables require expertise | Strength •Provide contextual knowledge •Valuable for sense disambiguation •User-centric results •More precise results Weakness •Difficult to construct |