Deep learning approaches for Arabic sentiment analysis

Mohammed, Ammar; Kora, Rania

doi:10.1007/s13278-019-0596-4

Cited by 79 publications

(49 citation statements)

References 35 publications

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“…New trends towards proposing LSTM-based approaches become increasingly prominent (Yuan et al. ( 2018 ); Mohammed and Kora ( 2019 ); Zhang and Zhang ( 2020 ); Bhoi et al. ( 2020 ); Ombabi et al.…”

Section: Research Contextmentioning

confidence: 99%

Word embeddings and deep learning for location prediction: tracking Coronavirus from British and American tweets

Hasni

Faïz

2021

Soc. Netw. Anal. Min.

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With the propagation of the Coronavirus pandemic, current trends on determining its individual and societal impacts become increasingly important. Recent researches grant special attention to the Coronavirus social networks infodemic to study such impacts. For this aim, we think that applying a geolocation process is crucial before proceeding to the infodemic management. In fact, the spread of reported events and actualities on social networks makes the identification of infected areas or locations of the information owners more challenging especially at a state level. In this paper, we focus on linguistic features to encode regional variations from short and noisy texts such as tweets to track this disease. We pay particular attention to contextual information for a better encoding of these features. We refer to some neural network-based models to capture relationships between words according to their contexts. Being examples of these models, we evaluate some word embedding ones to determine the most effective features’ combination that has more spatial evidence. Then, we ensure a sequential modeling of words for a better understanding of contextual information using recurrent neural networks. Without defining restricted sets of local words in relation to the Coronavirus disease, our framework called DeepGeoloc demonstrates its ability to geolocate both tweets and twitterers. It also makes it possible to capture geosemantics of nonlocal words and to delimit the sparse use of local ones particularly in retweets and reported events. Compared to some baselines, DeepGeoloc achieved competitive results. It also proves its scalability to handle large amounts of data and to geolocate new tweets even those describing new topics in relation to this disease.

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Section: Research Contextmentioning

confidence: 99%

Word embeddings and deep learning for location prediction: tracking Coronavirus from British and American tweets

Hasni

Faïz

2021

Soc. Netw. Anal. Min.

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“…The support vector machine is recommended for use as a prediction model, have been used successfully to predict SLA in different domains, especially it is the most successful supervised algorithms implemented [25]. we will use this model as a starting point for predict resources for each function to extract (number of tasks, task duration) as output to build demand curve by implement resource allocation algorithm for all-day flight.…”

Section: Support Vector Machinementioning

confidence: 99%

Optimized Planning of Resources Demand Curve in Ground Handling based on Machine Learning Prediction

Mamdouh¹,

Ezzat²,

Hefny³

2021

IJISA

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Determining the resource requirements at airports especially in-ground services companies is essential to successful planning in the future, which is represented in the resources demand curve according to the future flight schedule, through which staff schedules are created at the airport to cover the workload with ensuring the highest possible quality service provided. Given in the presence of variety service level agreements used on flight service vary according to many flight features, the resources assumption method makes planning difficult. For instance, flight position is not included in future flight schedule but it's efficacious in the identification of flight resources. In this regard, based on machine learning, we propose a model for building a resource demand curve for future flight schedules. It is divided into two phases, the first is the use of machine learning to predict resources of the service level agreement required on future flight schedules, and the second is the use of implement a resource allocation algorithm to build a demand curve based on predicted resources. This proposal could be applicable to airports that will provide efficient and realistic for the resources demand curve to ensure the resource planning does not deviate from the real-time resource requirements. the model has proven good accuracy when using one day of flights to measuring deviation between the proposed model predict demand curve when flights did not include the location feature and the actual demand curve when flights include location.

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“…1. Manual sentiment corpus construction, which is the approach applied in the construction of almost all the Arabic sentiment corpora [106,3,85,95,2,94,103,93], with the annotation being carried out, in the majority of cases, by native annotators.…”

Section: Work On Sentiment Lexicon and Corpora Constructionmentioning

confidence: 99%

ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages

Guellil

Azouaou

Chiclana

2020

Soc. Netw. Anal. Min.

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A corpus-based sentiment analysis approach for messages written in Arabic and its dialects is presented and implemented. The originality of this approach resides in the automation construction of the annotated sentiment corpus, which relies mainly on a sentiment lexicon that is also constructed automatically. For the classification step, shallow and deep classifiers are used with features being extracted applying word embedding models. For the validation of the constructed corpus, we proceed with a manual reviewing and it was found that 85.17% were correctly annotated. This approach is applied on the under-resourced Algerian dialect and the approach is tested on two external test corpora presented in the literature. The obtained results are very encouraging with an F1-score that is up to 88% (on the first test corpus) and up to 81% (on the second test corpus). These results respectively represent a 20% and a 6% improvement, respectively, when compared with existing work in the research literature.

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Deep learning approaches for Arabic sentiment analysis

Cited by 79 publications

References 35 publications

Word embeddings and deep learning for location prediction: tracking Coronavirus from British and American tweets

Word embeddings and deep learning for location prediction: tracking Coronavirus from British and American tweets

Optimized Planning of Resources Demand Curve in Ground Handling based on Machine Learning Prediction

ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages

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