SANA: Sentiment analysis on newspapers comments in Algeria

Rahab, Hichem; Zitouni, Abdelhafid; Djoudi, Mahiéddine

doi:10.1016/j.jksuci.2019.04.012

Cited by 27 publications

(20 citation statements)

References 28 publications

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“…The best results obtained on SANA_Alg are up to 81.00% (for F1 score). This result outperforms the results presented in the research literature, where the F1 score presented by Rahab et al [29] was up to 75%. Hence, our approach and corpus lead to an improvement of 6% on this corpus.…”

Section: Discussioncontrasting

confidence: 82%

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A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

et al. 2021

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In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach consists of constructing and applying a set of deep learning algorithms to classify the sentiment of Arabic messages as positive or negative. It was applied on Facebook messages written in Modern Standard Arabic (MSA) as well as in Algerian dialect (DALG, which is a low resourced-dialect, spoken by more than 40 million people) with both scripts Arabic and Arabizi. To handle Arabizi, we consider both options: transliteration (largely used in the research literature for handling Arabizi) and translation (never used in the research literature for handling Arabizi). For highlighting the effectiveness of a semi-supervised approach, we carried out different experiments using both corpora for the training (i.e. the corpus constructed automatically and the one that was reviewed manually). The experiments were done on many test corpora dedicated to MSA/DALG, which were proposed and evaluated in the research literature. Both classifiers are used, shallow and deep learning classifiers such as Random Forest (RF), Logistic Regression(LR) Convolutional Neural Network (CNN) and Long short-term memory (LSTM). These classifiers are combined with word embedding models such as Word2vec and fastText that were used for sentiment classification. Experimental results (F1 score up to 95% for intrinsic experiments and up to 89% for extrinsic experiments) showed that the proposed system outperforms the existing state-of-the-art methodologies (the best improvement is up to 25%).

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Section: Discussioncontrasting

confidence: 82%

“…The second one is PADIC corpus [28], which is a parallel corpus between MSA and many dialects, including DALG. The last one is SANA_Alg [29] that is a recent, an annotated sentiment corpus (which we are using for our experiments, to evaluate the proposed approach on a test corpus presented in the literature).…”

Section: Arab(ic+izi) Sentiment Analysis: Challengesmentioning

confidence: 99%

A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

et al. 2021

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“…The annotation process was manually carried out via crowdsourcing using the CrowdFlower platform [28]. In [29], a sentiment corpus SANA for the Algerian dialect was collected from the web. The corpus contains 513 comments which are classified into positive or negative classes.…”

Section: Sentiment Annotationmentioning

confidence: 99%

AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

et al. 2021

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At a time when research in the field of sentiment analysis tends to study advanced topics in languages, such as English, other languages such as Arabic still suffer from basic problems and challenges, most notably the availability of large corpora. Furthermore, manual annotation is time-consuming and difficult when the corpus is too large. This paper presents a semi-supervised self-learning technique, to extend an Arabic sentiment annotated corpus with unlabeled data, named AraSenCorpus. We use a neural network to train a set of models on a manually labeled dataset containing 15,000 tweets. We used these models to extend the corpus to a large Arabic sentiment corpus called “AraSenCorpus”. AraSenCorpus contains 4.5 million tweets and covers both modern standard Arabic and some of the Arabic dialects. The long-short term memory (LSTM) deep learning classifier is used to train and test the final corpus. We evaluate our proposed framework on two external benchmark datasets to ensure the improvement of the Arabic sentiment classification. The experimental results show that our corpus outperforms the existing state-of-the-art systems.

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“…Several studies suggested that sentiment analysis can be used to classify phenomenon in text data (Bringula et al, 2019;B. Liu, 2012;Miranda & Martin, 2020;Rahab et al, 2019;Ye et al, 2017).…”

Section: Analyses Of President's Speechesmentioning

confidence: 99%

Exploring Philippine Presidents’ speeches: A sentiment analysis and topic modeling approach

Miranda

Bringula

2021

Cogent Social Sciences

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This study analyzed the annual obligatory and traditional speeches, referred to as State of the Nation Address (SONA), of the 13 past Philippine presidents. The study determined the sentiments, as well as the emergent topics, expressed in these materials. It is found that these SONAs generally expressed positive sentiments while the lowest negative sentiment, on the other hand, was during the martial law period in 1974. Also, it is shown that "development" is the most frequently appeared word among these speeches. The study also revealed that the sentiments of the incoming presidents were lower than that of the outgoing. Moreover, it is shown that these SONAs mainly focused on the following concerns of the country: (a) economic development; (b) enhancement of public services; and (c) addressing challenges. The results of the study translate into the importance of SONA as a venue to discuss and to engage with its people the nation's state and direction.

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SANA: Sentiment analysis on newspapers comments in Algeria

Cited by 27 publications

References 28 publications

A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

Exploring Philippine Presidents’ speeches: A sentiment analysis and topic modeling approach

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