Analyzing Performance of Different Machine Learning Approaches With Doc2vec for Classifying Sentiment of Bengali Natural Language

Hoque, Md. Tazimul; Islam, Ashraful; Ahmed, Eshtiak; Mamun, Khondaker A.; Huda, Mohammad Nurul

doi:10.1109/ecace.2019.8679272

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…Small attempts in this aspect have been observed in this field. In the paper of classification on sentence level, Hoque et al [18] has been performed using doc2vec feature extraction technique with supervised machine learning classifiers. However, this approach on sentence level has achieved good accuracy of 77.72% by employing comparative analysis.…”

Section: B Sentiment Analysis In Bangla Languagementioning

confidence: 99%

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

2021

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We can state undoubtedly that Bangla language is rich enough to work with and implement various Natural Language Processing (NLP) tasks. Though it needs proper attention, hardly NLP field has been explored with it. In this age of digitalization, large amount of Bangla news contents are generated in online platforms. Some of the contents are inappropriate for the children or aged people. With the motivation to filter out news contents easily, the aim of this work is to perform document level sentiment analysis (SA) on Bangla online news. In this respect, the dataset is created by collecting news from online Bangla newspaper archive. Further, the documents are manually annotated into positive and negative classes. Composite process technique of “Pipeline” class including Count Vectorizer, transformer (TF-IDF) and machine learning (ML) classifiers are employed to extract features and to train the dataset. Six supervised ML classifiers (i.e. Multinomial Naive Bayes (MNB), K-Nearest Neighbor (K-NN), Random Forest (RF), (C4.5) Decision Tree (DT), Logistic Regression (LR) and Linear Support Vector Machine (LSVM)) are used to analyze the best classifier for the proposed model. There has been very few works on SA of Bangla news. So, this work is a small attempt to contribute in this field. This model showed remarkable efficiency through better results in both the validation process of percentage split method and 10-fold cross validation. Among all six classifiers, RF has outperformed others by 99% accuracy. Even though LSVM has shown lowest accuracy of 80%, it is also considered as good output. However, this work has also exhibited surpassing outcome for recent and critical Bangla news indicating proper feature extraction to build up the model.

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Section: B Sentiment Analysis In Bangla Languagementioning

confidence: 99%

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

2021

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“…Pool and Nissim [19] used Facebook reactions in a DS fashion to train an SVM for emotion detection. Nevertheless, they linked reactions to the original post, which is the most widely adopted association in studies that use Facebook reactions [20][21][22][23][24][25][26][27], rather than associating them to the comment, which is proposed in the present paper. In addition, they did not measure the reliability of the automatic tags.…”

Section: Related Workmentioning

confidence: 99%

“…This approach allows building a larger dataset by eliminating the need for extensive manual tagging. Some other studies [19][20][21][22][23][24][25][26][27] have already used Facebook reactions but, unlike this work, they linked the reaction to the topic from which it stemmed.…”

Section: Introductionmentioning

confidence: 99%

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

et al. 2021

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Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field.

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“…In [14], they use a Doc2Vec model in a corpus constructed with 7000 Bengali sentences, to analyze its feasibility in the Bengali sentiment analysis. The corpus consists of two types of data differentiated by their polarity, i.e., positive and negative.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison and Evaluation of Different Methods for the Feature Extraction from Educational Contents

et al. 2020

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This paper analyses the capabilities of different techniques to build a semantic representation of educational digital resources. Educational digital resources are modeled using the Learning Object Metadata (LOM) standard, and these semantic representations can be obtained from different LOM fields, like the title, description, among others, in order to extract the features/characteristics from the digital resources. The feature extraction methods used in this paper are the Best Matching 25 (BM25), the Latent Semantic Analysis (LSA), Doc2Vec, and the Latent Dirichlet allocation (LDA). The utilization of the features/descriptors generated by them are tested in three types of educational digital resources (scientific publications, learning objects, patents), a paraphrase corpus and two use cases: in an information retrieval context and in an educational recommendation system. For this analysis are used unsupervised metrics to determine the feature quality proposed by each one, which are two similarity functions and the entropy. In addition, the paper presents tests of the techniques for the classification of paraphrases. The experiments show that according to the type of content and metric, the performance of the feature extraction methods is very different; in some cases are better than the others, and in other cases is the inverse.

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Analyzing Performance of Different Machine Learning Approaches With Doc2vec for Classifying Sentiment of Bengali Natural Language

Cited by 14 publications

References 9 publications

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Comparison and Evaluation of Different Methods for the Feature Extraction from Educational Contents

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