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

Guellil, Imane; Azouaou, Faiçal; Chiclana, Francisco

doi:10.1007/s13278-020-00688-x

Cited by 10 publications

(4 citation statements)

References 91 publications

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“…This strategy was effective in enhancing the training evaluation and final predictions, resulting in an F1-score of 82%. In [15], this work presents a lexicon-based approach to build an annotated corpus for classifying the emotional state of Arabic messages using an Algerian Facebook dataset with 3048 messages (1488 positive and 1560 negative). The authors developed an algorithm that considers the morphology of Arabic and its dialects, along with a lexicon that was automatically made by using an existing English mood lexicon.…”

Section: Related Workmentioning

confidence: 99%

WeVoTe: A Weighted Voting Technique for Automatic Sentiment Annotation of Moroccan Dialect Comments

Matrane,

Benabbou,

Banou

2024

IEEE Access

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Sentiment analysis represents the systematic procedure of independently discerning polarity inherent in a textual document. A multitude of sectors can derive substantial advantages from this specialized domain. Conducting sentiment analysis (SA) involves various phases, with the initial step being the annotation process, which is often time-consuming and laborious. Within this framework, there exists a notable scarcity of existing research works. The complexity of this task becomes more difficult when analyzing texts in 'Darija', a form of the Moroccan dialect (MD). In our research endeavors, we introduced a novel automatic annotation methodology designed explicitly for sentiment analysis within the Moroccan dialect. A pivotal aspect of our contribution is the refinement of the stacking approach, utilizing a weighted voting technique for enhanced predictive accuracy. Our advanced method starts with the training of various neural network models across six unique MD datasets. The selection of these neural network architectures was underpinned by a comprehensive grid search procedure. Conclusively, it was discerned that models predicated on Recurrent Neural Networks (RNNs) outperformed others. Subsequent to this, we deployed an augmented stacking model, grounded in the aforementioned weighted voting technique. This model leverages the predictions generated by the neural networks as inputs. It then employs the mode of these inputs as an output, which feeds directly into a metaclassifier, which in turn produces the coefficients. These coefficients are then multiplicatively combined with the initial neural network predictions to derive the finale outputs. To evaluate the efficiency of our proposed methodology in annotating the six datasets, each dataset was isolated as a test while the remaining five served as training sets. Consequently, within the set of six datasets, the annotation results of three datasets have outperformed the established standards, attaining agreement rate percentages of 87.54% for MSAC, 91.25% for FB, 85.10% for MSDA, and 83.60% for MSTD, all of which represent new achievements in the literature.

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Section: Related Workmentioning

confidence: 99%

WeVoTe: A Weighted Voting Technique for Automatic Sentiment Annotation of Moroccan Dialect Comments

Matrane,

Benabbou,

Banou

2024

IEEE Access

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“…More recently, the study of [27] presented and tested an approach for automation building of the annotated sentiment corpus of messages written in Arabic and its dialects. They also summarized some sentiment analysis articles' result for Modern Standard Arabic (MSA).…”

Section: Arabic Sentiment Analysis Challengesmentioning

confidence: 99%

Sentiment Analysis of Algerian Dialect Using Machine Learning and Deep Learning with Word2vec

Mazari

Djeffal

2022

IJCAI

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This paper deals with the issue of sentiment analysis on dialectal comments extracted from social media. These comments concern the Algerian spoken language, written in Arabic and/or Latin characters, which could be either Modern Standard Arabic, French or local dialect. This complexity gives rise to a large number of text processing issues. The contributions of this work are fourfold. First, the building of the Algerian dialect sentiment dataset of 11760 comments collected from diverse social media platforms. Second, the creation of the Skip-Gram and CBOW models by word2vec from a corpus containing 466424 comments, these latter are used to enhance the sentiment dataset by semantically similar words. Third, the proposal of a set of preprocessing steps adapted to deal with dialectal texts. Finally, implementation and testing of different machine learning classifiers (SVM, Naive Bayes via its three variants (Bernoulli NB, Gaussian NB and Multinomial NB)) and two deep learning architectures (CNN, RNN) to evaluate and compare the dataset in original version, in a transcribed to Latin character version and then in a semantically-enhanced version by word2vec models. Experiments reach performances of sentiment classifiers applied on "dataset transcribed to Latin characters" of accuracies = (MNB:84.21%, CNN:64.11%) and on "transcribed dataset and enhanced by word2vec models" of accuracies = (SVM:83.70%, RNN:65.21%). Povzetek: Ta članek obravnava vprašanje analize sentimenta komentarjev alžirskega narečja, napisanih v arabščini in / ali latinici, pridobljenih iz družbenih medijev. Izvedbo izvajajo različne klasifikatorje strojnega učenja (SVM, Naive Bayes skozi njegove tri različice (Bernoulli, Gaussian in Multinomial)) in dve arhitekturi globokega učenja (CNN, RNN) za ovrednotenje in primerjavo nabora podatkov v prvotni različici, v različici, ki je prepisana v latinščini nato v semantično izboljšani različici modelov word2vec.

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“…During the last decade, the computer science field witnessed a considerable resort to word embedding models Arora and Kansal (2019), Tshimula et al (2020), Guellil et al (2020), Kejriwal and Zhou (2020).The principle of these models (also referred to as distributed word representation) is to map related words to nearby points in the space given a corpus of relationships. That is to say, words occurring in similar contexts have similar vector representations and geometric distances between them reflect the degree of their relationships.…”

Section: Word Embedding Modelsmentioning

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

Cited by 10 publications

References 91 publications

WeVoTe: A Weighted Voting Technique for Automatic Sentiment Annotation of Moroccan Dialect Comments

WeVoTe: A Weighted Voting Technique for Automatic Sentiment Annotation of Moroccan Dialect Comments

Sentiment Analysis of Algerian Dialect Using Machine Learning and Deep Learning with Word2vec

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

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