NgramSPD: Exploring optimal n-gram model for sentiment polarity detection in different languages

Graovac, Jelena; Mladenović, Marko Z.; Tanasijević, Ivana

doi:10.3233/ida-183879

Cited by 10 publications

(6 citation statements)

References 34 publications

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“…Finally, text that needs to be analyzed is input into the trained model to obtain the sentiment classification results. The most commonly used feature methods include N-Gram feature [10], POS feature [11], TF-IDF feature [12], and most commonly used classification methods include KNN [13], Naive Bayesian [14], SVM [15]. Pang et al [16] pioneered the application of machine learning in sentiment classification.…”

Section: Sentiment Classification Methodsmentioning

confidence: 99%

Bi-GRU Sentiment Classification for Chinese Based on Grammar Rules and BERT

Lu¹,

Zhu²,

Xu³

et al. 2020

IJCIS

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Sentiment classification is a fundamental task in NLP, and its aim to predict the sentiment polarities of the given texts. Recent researches show great interest in modeling Chinese sentiment classification. However, the complexity of Chinese grammar makes the performance of the existing Chinese sentiment classification model not perform well. In order to address the above problem, we propose a sentiment classification method based on grammar rules and bidirectional encoder representation from transformers (BERT). We first preprocess data through BERT model. Then we combine the Chinese grammar rules with Bi-gated recurrent neural network (GRU) in the form of constraints, and simulate the linguistic functions at the sentence by standardizing the output of adjacent positions. Extensive experiments on two public datasets demonstrate the effectiveness of our proposed method, and our findings in the experiment provide new insights for the future development of Chinese sentiment classification.

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Section: Sentiment Classification Methodsmentioning

confidence: 99%

Bi-GRU Sentiment Classification for Chinese Based on Grammar Rules and BERT

Lu¹,

Zhu²,

Xu³

et al. 2020

IJCIS

View full text Add to dashboard Cite

show abstract

“…Machine learning-based sentiment polarity detection in seven languages, including Arabic, is studied in [ 19 ]. A set of n-gram models is tested for byte, character, and word level.…”

Section: Related Workmentioning

confidence: 99%

“…The text is mapped in this step to a vector representation. This representation facilitates the machine handling of textual data [ 19 ]. Several vector representation models are proposed in the literature [ 74 , 75 ].…”

Section: Proposed Approachmentioning

confidence: 99%

“… , where k represents the vocabulary, i.e., the set of all words in the used dataset. refers to the presence “1” or the absence “0” of the word “ j ” in the document “ i ,” in other terms whether this word contributes to the representation of this document [ 19 ]. This phase produces a vector of 698 attributes.…”

Section: Proposed Approachmentioning

confidence: 99%

“…We found in the literature several used approaches to resolve the problem of ASA. However, one can group the available approaches into two main categories [ 19 ]: machine learning or corpus-based [ 20 – 22 ] and lexicon-based approaches [ 23 – 27 ].…”

Section: Introductionmentioning

confidence: 99%

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Rule-Based Arabic Sentiment Analysis using Binary Equilibrium Optimization Algorithm

Rahab

Haouassi

Laouid³

2022

Arab J Sci Eng

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With the development of websites and social networks, Internet users generate a massive amount of comments and information on the Web. Sentiment analysis, also called opinion mining, offers an opportunity to mine the people’s sentiments and emotions from the textual comments. In the last decade, sentiment analysis has been applied in research areas such as recommendation and support systems and has become an area of interest for many researchers. Therefore, many studies have been carried out on English, while other languages, such as Arabic, received less attention. Increasingly, sentiment analysis researchers use machine learning due to its excellent performance. However, the generated models are black boxes and non-interpretable by the users. The rule-based classification is a promising approach for generating interpretable models. This work proposes a classification rule-based Arabic sentiment analysis approach together with a new binary equilibrium optimization metaheuristic algorithm as an optimization method for classification rule generation from Arabic documents. The proposed approach has been experimented on the Opinion Corpus for Arabic (OCA) and generates a classification model of thirteen rules. The comparison results with state-of-the-art methods show that the proposed approach outperforms all other white-box models regarding classification accuracy.

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