The basis of the analysis for this paper was applying the classifier fastText to the two tasks: tag predictions and sentiment analysis, and comparing its performance and efficiency with other text classifiers. The simplicity of linear classifiers allows a model to be scaled to very large data set while maintaining its good performance. The authors suggest that linear classifiers are very effective if the right features are used. The motivation for this paper is to determine whether a simpler text classifier, which is inexpensive in terms of training and test time, can approximate the performance of these more complex neural networks.
However, it is slow at both training and testing time, therefore limiting their usage for very large datasets. Neural networks have been utilized more recently for Text-Classifications and demonstrated very good performances. When a user searches a specific word that best describes the content they are looking for, text classification helps with categorizing the appropriate content. An example of an application of text classification is web search and content ranking. Text Classification is utilized by millions of web users on a daily basis. 5.1.3 Comparison with (Zhang et al., 2015) based on recurrent networks.2.1 Natural-Language Processing and Text Classification.
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