Stemming Algorithm for Arabic Text Using a Parallel Data Processing

Bougar, Marieme; Ziyati, El Houssaine

doi:10.1007/978-981-13-1165-9_23

Cited by 2 publications

(2 citation statements)

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“…Preprocessing Data: As illustrated in Table 1, this step consists first of translating the different words composing the comments in Arabic text pasted in their roots. To perform this step, we chose KHOJA, an algorithm for preprocessing data based on the roots of the words, we used KHOJA because it is an algorithm that we have perform in many previews work [16], it shows its effectiveness in term of accuracy, available and simple to implement. This algorithm processes the words under the following major steps:…”

Section: Problematicmentioning

confidence: 99%

“…In several of our published works, we have been particularly interested in the pre-processing of this data in the Arabic language [2], hence our interest in this research to move to the classification phase. Therefore, in this paper, we treat the analysis of textual data including the polarity of opinions (positive, negative, or neutral) and machine learning.…”

Section: Introductionmentioning

confidence: 99%

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Analysis Methods and Classification Algorithms with a Novel Sentiment Classification for Arabic Text using the Lexicon-Based Approach

Marieme¹,

Houssaine²

2022

Adv. sci. technol. eng. syst. j.

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Social networks have become a valuable platform for tracking and analyzing Internet users' feelings. This analysis provides crucial information for decision-making in various areas, such as politics and marketing. In addition to this challenge and our interest in the field of big data and sentiment analysis in social networks, we have dedicated this work to combine different aspects of methods or techniques leading to the facilitation of feelings classification in social networks, including text analysis and sentiment analysis. We expose the approaches and the algorithms of supervised machine learning for the classification of feelings. We further our research to concisely present the methods of data representation and the parameters used to evaluate a sentiment analysis method in the context of social networks, with a section presenting our novel lexicon-based approach to give more accurate results in classifying Arabic text. The proposed approach has shown a promising accuracy percentage, especially the precision of the sentiment detected from text with F-Score up to 66%.

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Section: Problematicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%