ANT Corpus: An Arabic News Text Collection for Textual Classification

ResumenEste trabajo se centra en la revisión crítica de los principales modelos tipológicos destinadosa la clasificación de los textos, teniendo por objetivos identificar las diferencias entreunos y otros, dar cuenta de la evolución en los criterios utilizados para la clasificación textual y entregar una aproximación conceptual de cada clase o tipo de texto. El análisis de los planteamientos referidos a la determinación de los tipos de textos muestra cómo se trata un ámbito de la lingüística que ha cobrado especial impulso a partir de los años setenta, considerándose tipologías disímiles para resolver la complejidad del texto.Palabras clave: Clase textual - tipo de texto - tipología textual – criterios de clasificación textualAbstractThis research in centered on the review of the principal tipological models destined to the classification of the texts, begin for objetive identify the difference between some andothers.Show the evolution in the criteria used for the textual classification and delivereda conceptual approximation of class and tipe of texts. The analysis of the approchesrecounted to the determination of tipes of texts show how it is, and question an areaof the linguistics that area has resived and special impulse from the seventies begin considered dissimilar to solve the complexity of the text.Keywords: Textual class, tipe of texts, textual typology, criteria of texual classification

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Text Classification

Handbook of Research on Machine Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2229-4.ch033 ◽

2017 ◽

pp. 740-761

Author(s):

Ben Elfadhl Mohamed Ahmed ◽

Ben Abdessalem Wahiba

Keyword(s):

Decision Trees ◽

Text Classification ◽

Fuzzy Decision ◽

Text Documents ◽

Fuzzy Decision Trees ◽

Textual Classification ◽

Automatic Text

In this chapter, a supervised automatic text documents classification using the fuzzy decision trees technique is proposed. Whatever the algorithm used in the fuzzy decision trees, there must be a criterion for the choice of discriminating attribute at the nodes to partition. For fuzzy decision trees usually two heuristics were used to select the discriminating attribute at the node to partition. In the field of text documents classification there is a heuristic that has not yet been tested. This chapter tested this heuristic. The latter was presented in the works of Yuan and Shaw (1995) and was applied in a context different then the textual classification. This heuristic is analyzed and adapted to the author's approach for text documents classification.

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Enhanced Long Short-Term Memory (ELSTM) Model for Sentiment Analysis

The International Arab Journal of Information Technology ◽

10.34028/iajit/18/6/12 ◽

2021 ◽

Author(s):

Dimple Tiwari ◽

Bharti Nagpal

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Parameter Tuning ◽

Short Term ◽

Term Memory ◽

Automatic Feature Extraction ◽

Long Short Term Memory ◽

Textual Classification

Sentiment analysis is used to embed an extensive collection of reviews and predicts people's opinion towards a particular topic, which is helpful for decision-makers. Machine learning and deep learning are standard techniques, which make the process of sentiment analysis simpler and popular. In this research, deep learning is used to analyze the sentiments of people. It has an ability to perform automatic feature extraction, which provides better performance, a more vibrant appearance, and more reliable results than conventional feature-based techniques. Traditional approaches were based on complicated manual feature extractions that were not able to provide reliable results. Therefore, the presented study aimed to improve the performance of the deep learning approach by combining automatic feature extraction with manual feature extraction techniques. The enhanced ELSTM model is proposed with hyper-parameter tuning in previous Long Short-Term Memory (LSTM) to get better results. Based on the results, a novel model of sentiment analysis and novel algorithm are proposed to set the benchmark in the field of textual classification and to describe the procedure of the developed model, respectively. The results of the ELSTM model are presented by training and testing accuracy curve. Finally, a comparative study confirms the best performance of the proposed ELSTM model.

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