Sentiment Analysis in English Texts

Alshamsi, Arwa; Bayari, Reem; Salloum, Said A.

doi:10.25046/aj0506200

Cited by 41 publications

(22 citation statements)

References 33 publications

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“…As similar words can be expressed differently in each context, this approach with limited words dictionary may not yield acceptable results for every other news or reviews dataset. The work of Alshamsi et al [9] in 2020 mainly focused on analyzing behavior of ML classifiers in balanced and unbalanced datasets to extract sentiments from airline tweets. Although among 6 classifiers Naïve Bayes and ID3 showed good results with balanced dataset, size can greatly impact to build up a good model with ML classifiers.…”

Section: A Sentiment Analysis In English and Other Languagesmentioning

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: A Sentiment Analysis In English and Other Languagesmentioning

confidence: 99%

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

2021

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“…Social Media has become a part of daily life; It is increasingly hard to survive in this modern era of digital media without a digital footprint [1]. People are increasingly using social media platforms for various purposes and reasons, resulting in a vast amount of online data being created on a daily basis [2]. Arabic is a Semitic most commonly utilized language around the world, it is the official language in 28 countries [3].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review for Sentiment Analysis of Arabic Dialect Texts Researches

Shamsi

Abdallah

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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People in the Arab world prefer to use their dialects in writing while using social media sites which results in generating a significant amount of texts. Sentiment analysis in texts is one of the most important applications of the Natural Language Processing field of science. Sentiment Analysis involves classifying texts based upon emotions or polarity. Different approaches have been utilized so far for Sentiment Analysis of Arabic Texts. In this systematic review, the author aims to explore the researches and studies that were in the field of Sentiment Analysis of Arabic Dialects to report the utilized approaches, constructed datasets, most common type of dialects explored by researchers, and reported results of Sentiment Analysis in term of accuracy, recall, precision, and F-Measure. Author reported the following findings for this systematic review: (1) in most of the included research papers the dialect type is not specified and mostly it was mentioned (Arabic Dialects) in general, however, some other dialects were explored specifically by authors such as Saudi Dialect (2) 50% of the utilized datasets in the included research papers are constructed by the authors themselves, moreover, the size of most of the utilized datasets are between 10,000 and 50,000, and very limit number of datasets had size above 100,000, (3) machine learning algorithms (classifiers) are the most common approach that were used for Sentiment Analysis of Arabic Dialects (4) the best result for Sentiment Analysis of Arabic Dialects achieved while different machine learning algorithms (classifiers) were used, and (5) among social media platforms, Twitter is the most common utilized online platform for constructing datasets for Arabic Dialects texts.

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“…By constructing the feature set, the classifier is trained on the feature set of the training set, and the sentiment label is output for the unlabeled text. In recent years, the commonly used algorithms for text sentiment analysis and text classification include deep learning (Chen et al, 2020;Zhang et al, 2020), decision tree (Almunirawi and Maghari, 2016), support vector machine (Ahmad et al, 2017), sparse representation (Unnikrishnan et al, 2019;Gu et al, 2020), KNN classifier (Bozkurt et al, 2019), Naïve Bayes (Alshamsi et al, 2020), fuzzy logic (Chaturvedi et al, 2019), and extreme learning machine (Lauren et al, 2018;Waheeb et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Sentiment Classification of News Text Data Using Intelligent Model

Zhang¹

2021

Front. Psychol.

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Text sentiment classification is a fundamental sub-area in natural language processing. The sentiment classification algorithm is highly domain-dependent. For example, the phrase “traffic jam” expresses negative sentiment in the sentence “I was stuck in a traffic jam on the elevated for 2 h.” But in the domain of transportation, the phrase “traffic jam” in the sentence “Bread and water are essential terms in traffic jams” is without any sentiment. The most common method is to use the domain-specific data samples to classify the text in this domain. However, text sentiment analysis based on machine learning relies on sufficient labeled training data. Aiming at the problem of sentiment classification of news text data with insufficient label news data and the domain adaptation of text sentiment classifiers, an intelligent model, i.e., transfer learning discriminative dictionary learning algorithm (TLDDL) is proposed for cross-domain text sentiment classification. Based on the framework of dictionary learning, the samples from the different domains are projected into a subspace, and a domain-invariant dictionary is built to connect two different domains. To improve the discriminative performance of the proposed algorithm, the discrimination information preserved term and principal component analysis (PCA) term are combined into the objective function. The experiments are performed on three public text datasets. The experimental results show that the proposed algorithm improves the sentiment classification performance of texts in the target domain.

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Sentiment Analysis in English Texts

Cited by 41 publications

References 33 publications

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

A Systematic Review for Sentiment Analysis of Arabic Dialect Texts Researches

Sentiment Classification of News Text Data Using Intelligent Model

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