An automated text categorization framework based on hyperparameter optimization

Abstract

A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackle using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any supervised learning problem, whereas others are specifically designed to tackle a particular task, using complex and computational expensive processes such as lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we propose a minimalistic and wide system able to tackle text classification tasks independent of domain and language, namely µTC. It is composed by some easy to implement text transformations, text representations, and a supervised learning algorithm. These pieces produce a competitive classifier even in the domain of informally written text. We provide a detailed description of µTC along with an extensive experimental comparison with relevant state-of-the-art methods. µTC was compared on 30 different datasets. Regarding accuracy, µTC obtained the best performance in 20 datasets while achieves competitive results in the remaining 10. The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution. Furthermore, it is important to state that our approach allows the usage of the technology even without knowledge of machine learning and natural language processing.

Text recommendation based on time series and multi-label information

“…Gupta S et al came up with a recommendation method based on the theme and the core idea of the text [11], in their method, the user had to provide a full text (including title, abstract, text and reference) for extracting the core idea. Similarly, Tellez E S et al introduced an independent framework to recommend the useful text [12], in which users also had to enter a full academic literature text to generate some different information, and then submitted it to the existing network information resources to realize the recommendation. Mäntylä M V et al took the cited text abstract, introduction and conclusion to obtain better recommendation results [13], however, these methods not only actually increased the user's burden, but also could not be able to provide extra information on the basis of a section of the user's interested information in the real environment.…”

Semantic Ontology-Based Approach to Enhance Arabic Text Classification

“…Topic discovery, text categorization, or text classification can be defined as the process of labeling unstructured data (e.g., in webpages or documents) by using pre-defined categories in order to define such data and organize them [21][22][23][24][25][26]. A lot of work has been done to study the different aspects of text classification techniques and their usage in the knowledge discovery.…”

MineriaUNAM at SemEval-2019 Task 5: Detecting Hate Speech in Twitter using Multiple Features in a Combinatorial Framework

“…For task A, we used a combinatorial framework (µT C) developed by Tellez et al (2018). The framework approaches any text classification problem as a combinatorial optimization problem; where there is a search space containing all possible combinations of different text transformations (tokenizers) and weighting schemes with their respective parameters, and, on this search space, a meta-heuristic is used to search for a configuration that produces a highly effective text classifier.…”