Sentiment analysis: Arabic sentiment lexicons

Sabra, Khaled S.; Zantout, Rached; Abed, Mohamad A. El; Hamandi, Lama

doi:10.1109/senset.2017.8125054

Cited by 23 publications

(6 citation statements)

References 6 publications

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“…On the contrary, a lexicon is created automatically or manually for obtaining datasets with different n-grams and weights. As presented in [26], an Arabic sentiment analysis lexicon is created by assigning a score for each Arabic term with its corresponding term in the English language. Each term is classified according to its polarity and the polarity score is added to each term to define the orientation of the sentence.…”

Section: Unsupervised Sentiment Analysismentioning

confidence: 99%

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Mostafa¹

2023

Intelligent Automation &Amp; Soft Computing

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Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects. Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy. The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms. Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process. In unsupervised mechanisms, a lexicon is constructed for storing polarity terms. The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms. In addition, most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis process. Applying both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging process. This paper provides a comprehensive evaluation of polarity weights and mechanisms for recent sentiment analysis research. A semi-supervised framework is applied for processing data using both lexicon and machine learning algorithms. An interactive sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic terms. An enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities automatically. The experimental results are conducted on two datasets to measure the overall accuracy of proposed algorithms that achieved high results when compared to machine learning algorithms.

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Section: Unsupervised Sentiment Analysismentioning

confidence: 99%

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Mostafa¹

2023

Intelligent Automation &Amp; Soft Computing

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“…Once we obtained the suitable word vectors from the skipgram model, the neural network takes the output features as inputs and applies two convolutional layers to the features to learn context information of the words. The convolution operation is described as formula (1).…”

Section: E Features Extractionmentioning

confidence: 99%

“…Sentiment analysis is known as the extraction and interpretation of opinions expressed in a text written in a natural language on a certain subject [1]. Recently, sentiment analysis and opinion mining became extremely popular due to the increase of social network usage, which led to produce a huge number of texts.…”

Section: Introductionmentioning

confidence: 99%

Detecting Irony in Arabic Microblogs using Deep Convolutional Neural Networks

Alhaidari¹,

Alyoubi²,

Alotaibi³

2022

IJACSA

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A considerable amount of research has been developed lately to analyze social media with the intention of understanding and exploiting the available information. Recently, irony has took a significant role in human communication as it has been increasingly used in many social media platforms. In Natural Language Processing (NLP), irony recognition is an important yet difficult problem to solve. It is considered to be a complex linguistic phenomenon in which people means the opposite of what they literally say. Due to its significance, it becomes essential to analyze and detect irony in subjective texts to improve the analysis tools to classify people opinion automatically. This paper explores how deep learning methods can be employed to the detection of irony in Arabic language with the help of Word2vec term representations that converts words to vectors. We applied two different deep learning models; Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). We tested our frameworks with a manually annotated datasets that was collected using Tweet Scraper. The best result was achieved by the CNN model with an F1 score of 0.87.

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“…Despite a relatively large number of works devoted to comparing approaches and methods for sentiment analysis, most of them study only some aspects of solving the problem [5][6][7][8][9][10][11][12]. For example, in [5], the authors compare the approach based on the lexical algorithm from the Apache Hadoop architecture and Stanford coreNLP library with the implementation of recursive neural networks.…”

Section: Related Workmentioning

confidence: 99%

Generalized approach to sentiment analysis of short text messages in natural language processing

Polyakov¹,

Voskov²,

Abramov³

et al. 2020

ICS

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Introduction: Sentiment analysis is a complex problem whose solution essentially depends on the context, field of study andamount of text data. Analysis of publications shows that the authors often do not use the full range of possible data transformationsand their combinations. Only a part of the transformations is used, limiting the ways to develop high-quality classification models.Purpose: Developing and exploring a generalized approach to building a model, which consists in sequentially passing throughthe stages of exploratory data analysis, obtaining a basic solution, vectorization, preprocessing, hyperparameter optimization, andmodeling. Results: Comparative experiments conducted using a generalized approach for classical machine learning and deeplearning algorithms in order to solve the problem of sentiment analysis of short text messages in natural language processinghave demonstrated that the classification quality grows from one stage to another. For classical algorithms, such an increasein quality was insignificant, but for deep learning, it was 8% on average at each stage. Additional studies have shown that theuse of automatic machine learning which uses classical classification algorithms is comparable in quality to manual modeldevelopment; however, it takes much longer. The use of transfer learning has a small but positive effect on the classificationquality. Practical relevance: The proposed sequential approach can significantly improve the quality of models under developmentin natural language processing problems.

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Sentiment analysis: Arabic sentiment lexicons

Cited by 23 publications

References 6 publications

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Detecting Irony in Arabic Microblogs using Deep Convolutional Neural Networks

Generalized approach to sentiment analysis of short text messages in natural language processing

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