Key issues in conducting sentiment analysis on Arabic social media text

Ahmed, Salma; Pasquier, M.; Qadah, Ghassan Z.

doi:10.1109/innovations.2013.6544396

Cited by 44 publications

(23 citation statements)

References 9 publications

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“…The proposed tool possesses many features such as the way negation and intensification. Another sentiment analysis methodology based on Arabic social media is presented in [13]. The authors of this research present taxonomy of sentiment analysis classification.…”

Section: Arabic Setiment Analysismentioning

confidence: 99%

An Evaluation of Sentiment Analysis and Classification Algorithms for Arabic Textual Data

Mostafa¹

2017

IJCA

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Sentiment analysis is a recent advance in text mining applications for analyzing textual data according to orientation of human comments to determine whether they are positive, negative, or neutral. Different data mining techniques and algorithms such as support vector machine, naïve Bayes, decision tree, k-nearest neighbor and other techniques are used for analyzing textual data. These techniques are evaluated based on Arabic language due to its richness and diversity that can lead to difficulties in analyzing and mining large number of morphological and linguistic words that can lead to different meaning. This research provides sophisticated categorization of most or all recent articles according to the algorithms used in analyzing sentiment data. A comparison table for the proposed algorithms is presented that explains each algorithm and its use in mining and analysis of Arabic textual data and provides different evaluation for each sentiment analysis and classification algorithm according to different categories such as sentiment type, feature selection, sentiment polarity, domain oriented , data scope and data source, algorithm used in the sentiment analysis or classification, and the best algorithm result during the analysis and mining process. The experimental results explain that support vector machine algorithm presents high accuracy with approximately 77% when compared to other text mining algorithms. Different algorithms of sentiment analysis and classifications are evaluated based on their use in Arabic language which has not been evaluated before.

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Section: Arabic Setiment Analysismentioning

confidence: 99%

An Evaluation of Sentiment Analysis and Classification Algorithms for Arabic Textual Data

Mostafa¹

2017

IJCA

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“…Table 1 below categorizes the surveyed Arabic SA studies into these classes, and fig.1 shows the result. [7], [9], [10], [11], [13], [19], [20], [27], [28], [29], [33], [41], [42], [48], [49], [52], [65], [69] Arabic sentiment analysis studies used different method based on the used approach, some of these methods considered as a dominance. We have collected the various methods used in the different Arabic SA studies for supervised, semi-supervised, unsupervised and hybrid experiments and presented the result in Table 2.…”

Section: B Arabic Sentiment Analysismentioning

confidence: 99%

“…It is more common to find studies where a single type of data was used, but there are a number of cases where multiple data types were combined. 4 Reviews/Opinions [7], [11], [19], [23], [24], [33], [39], [42], [48], [49] 5 Social Comments/Social News [2], [4], [6], [10], [27], [65], [69], [70] The Arabic language has many dialects, and no study of Arabic sentiment analysis is complete without a review of the different dialects covered in the studies. Table 4 below presents an overview of the Arabic dialect distribution in the studies surveyed.…”

Section: B Arabic Sentiment Analysismentioning

confidence: 99%

“…Table 4 below presents an overview of the Arabic dialect distribution in the studies surveyed. As we can see from the above [24], [29], [70] 2 SVM + NB [7], [9], [10], [11], [27], [39], [41], [42], [48] Also we can see that the Saudi dialect was not given attention by the researchers. [2], [3], [4], [5], [6], [9], [13], [19], [20], [22], [23], [24], [33], [39], [41], [42], [46], [48], [49], [65], [70] 2 MSA (Egyptian) [7], [10], [11], [21], [27], [28], [51], [70] 3 MSA (Levantine) [7] 4 MSA (Khaliji) [7], [11], [65] 5 MSA (Arabizi) [7] 6 MSA (Mesopotamian) [11] 7…”

Section: B Arabic Sentiment Analysismentioning

confidence: 99%

“…Most of the observations recorded during this survey generally lead to the conclusion that Arabic sentiment analysis is in its growing phases. S/N Processing Level Studies 1 Sentence-level [2], [3], [23], [52], [69], [70] 2 Document-level [1], [4], [5], [6], [7], [9], [10], [11], [13], [19], [20], [21], [22], [27], [28], [29], [32], [33], [39], [41], [42], [46], [48], [49], [51], [65], 3 Document-level + Sentence-level [24] 79 | P a g e www.ijacsa.thesai.org [7] showed extensive list of features, studied the importance of different features could try more classification methods, no details given on how sampling is conducted to obtain a balanced subset of data [9] Pre-processing leads to improvement Tags need to be added manually [10] Introduced Social Network specific features Dialects are not considered [11] Besides classification on subjectivity and polarity, also considered intensity classification Does not deal with Emoticons, chat language and Arabizi [13] Large-scale lexicon Could try more classifiers, dialects not considered [19] Studied the effects of pre-processing and the characteristics of the dataset Could try more classifiers, dialects not considered [20] Developed th...…”

Section: B Arabic Sentiment Analysismentioning

confidence: 99%

See 2 more Smart Citations

Arabic Sentiment Analysis: A Survey

Assiri¹,

Emam²,

Al-Dossari³

2015

ijacsa

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Abstract-Most social media commentary in the Arabic language space is made using unstructured non-grammatical slang Arabic language, presenting complex challenges for sentiment analysis and opinion extraction of online commentary and micro blogging data in this important domain. This paper provides a comprehensive analysis of the important research works in the field of Arabic sentiment analysis. An in-depth qualitative analysis of the various features of the research works is carried out and a summary of objective findings is presented. We used smoothness analysis to evaluate the percentage error in the performance scores reported in the studies from their linearly-projected values (smoothness) which is an estimate of the influence of the different approaches used by the authors on the performance scores obtained. To solve a bounding issue with the data as it was reported, we modified existing logarithmic smoothing technique and applied it to pre-process the performance scores before the analysis. Our results from the analysis have been reported and interpreted for the various performance parameters: accuracy, precision, recall and F-score.

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AMUSED: An Annotation Framework of Multimodal Social Media Data

Shahi

Majchrzak

2022

Communications in Computer and Information Science

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Key issues in conducting sentiment analysis on Arabic social media text

Cited by 44 publications

References 9 publications

An Evaluation of Sentiment Analysis and Classification Algorithms for Arabic Textual Data

An Evaluation of Sentiment Analysis and Classification Algorithms for Arabic Textual Data

Arabic Sentiment Analysis: A Survey

AMUSED: An Annotation Framework of Multimodal Social Media Data

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