Classification of Textual Sentiment Using Ensemble Technique

Mamun, Md. Mashiur Rahaman; Sharif, Omar; Hoque, Mohammed Moshiul

doi:10.1007/s42979-021-00922-z

Cited by 14 publications

(6 citation statements)

References 37 publications

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“…The effectiveness of the machine learning algorithms is evaluated, and the results show that NB performs better than SVM. According to the findings of Mamun et al [17], the accuracy of the ensemble approached (LR+SVM+RF) with frequency-inverse document frequency features was 82% higher than that of the other simple classifier models on the dataset that was constructed. Figure 1 illustrates the several machine-learning approaches for SA [8].…”

Section: Related Workmentioning

confidence: 92%

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Hicham

Karim

Habbat

2023

IJECE

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<span lang="EN-US">Arabic’s complex morphology, orthography, and dialects make sentiment analysis difficult. This activity makes it harder to extract text attributes from short conversations to evaluate tone. Analyzing and judging a person’s emotional state is complex. Due to these issues, interpreting sentiments accurately and identifying polarity may take much work. Sentiment analysis extracts subjective information from text. This research evaluates machine learning (ML) techniques for understanding Arabic emotions. Sentiment analysis (SA) uses a support vector machine (SVM), Adaboost classifier (AC), maximum entropy (ME), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), logistic regression (LR), and naive Bayes (NB). A model for the ensemble-based sentiment was developed. Ensemble classifiers (ECs) with 10-fold cross-validation out-performed other machine learning classifiers in accuracy (A), specificity (S), precision (P), F1 score (FS), and sensitivity (S).</span><p> </p>

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Section: Related Workmentioning

confidence: 92%

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Hicham

Karim

Habbat

2023

IJECE

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“…Adaptive boosting (AdaBoost) classifier is a well-known iterative ensemble approach that tends to give good performance with weak learning classifiers. The researcher including [21] and [32] also used this approach for textual sentiment classification. This research has applied AdaBoost Classifier for the identification and classification of the crime tweets and it gives a good performance with the TF-IDF vectorizer with an accuracy of 91.6% on the testing dataset.…”

Section: Ensemble Approachesmentioning

confidence: 99%

“…The random forest with TF-IDF unigram exhibits the highest precision of 77%. Research (including [21], [22], and [23] and inter alia) shows that the ensemble approach has a high tendency to outperform the machine learning algorithms for the identification and classification of tweets. In contrast to the research work, this research has applied different settings of the ensemble approach to the dataset for the identification and classification of crime tweets.…”

Section: Introductionmentioning

confidence: 99%

An ensemble approach for the identification and classification of crime tweets in the English language

Siddiqui,

Hina,

Asif

et al. 2023

Comput Sci Inf Technol

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Twitter is a famous social media platform, which supports short posts limited to 280 characters. Users tweet about many topics like movie reviews, customer service, meals they just ate, and awareness posts. Tweets carrying information about some crime scenes are crime tweets. Crime tweets are crucial and informative and separate classification is required. Identification and classification of crime tweets is a challenging task and has been the researcher’s latest interest. The researchers used different approaches to identify and classify crime tweets. This research has used an ensemble approach for the identification and classification of crime tweets. Tweepy and Twint libraries were used to collect datasets from Twitter. Both libraries use contrasting methods for extracting tweets from Twitter. This research has applied many ensemble approaches for the identification and classification of crime tweets. Logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and random forest (RF) Classifier assigned with the weights of 1,2,1,1 and 1 respectively ensemble together by a soft weighted Voting classifier along with term frequency – inverse document frequency (TF-IDF) vectorizer gives the best performance with an accuracy of 96.2% on the testing dataset.

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“…Purba et al [27] proposed an emotion detection system employing a Multinomial Naive Bayes classifier to identify emotions into three categories (angry, sad, and happy) with an accuracy of 68.27%. Mamun et al [28] introduced a sentiment dataset comprising 8122 text expressions categorized into negative, positive, and neutral. They showed that the ensemble technique (LR+RF+SVM) surpassed the other approaches attaining the most increased accuracy of 82%.…”

Section: Related Workmentioning

confidence: 99%

TEmoX: Classification of Textual Emotion Using Ensemble of Transformers

Das,

Hoque,

Sharif

et al. 2023

IEEE Access

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Textual emotion classification (TxtEC) refers to the classification of emotion expressed by individuals in textual form. The widespread use of the Internet and numerous Web 2.0 applications has emerged in an expeditious growth of textual interactions. However, determining emotion from texts is challenging due to their unorganized, unstructured, and disordered forms. While research in textual emotion classification has made considerable breakthroughs for highresource languages, it is yet challenging for low-resource languages like Bengali. This work presents a transformer-based ensemble approach (called TEmoX) to categorize Bengali textual data into six integral emotions: joy, anger, disgust, fear, sadness, and surprise. This research investigates 38 classifier models developed using four machine learning LR, RF, MNB, SVM, three deep-learning CNN, BiLSTM, CNN+BiLSTM, five transformer-based m-BERT, XLM-R, Bangla-BERT-1, Bangla-BERT-2, and Indic-DistilBERT techniques with two ensemble strategies and three embedding techniques. The developed models are trained, tuned, and tested on the three versions of the Bengali emotion text corpus BEmoC-v1, BEmoC-v2, BEmoC-v3. The experimental outcomes reveal that the weighted ensemble of four transformer models En-22: Bangla-BERT-2, XLM-R, Indic-DistilBERT, Bangla-BERT-1 outperforms the baseline models and existing methods by providing the maximum weighted F 1-score (80.24%) on BEmoC-v3. The dataset, models, and fractions of codes are available at https://github.com/avishek-018/TEmoX.

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Classification of Textual Sentiment Using Ensemble Technique

Cited by 14 publications

References 37 publications

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

An ensemble approach for the identification and classification of crime tweets in the English language

TEmoX: Classification of Textual Emotion Using Ensemble of Transformers

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