Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

Kumar, Sanjeev; Singh, Ravendra; Khan, Mohammad Zubair; Noorwali, Abdulfattah

doi:10.7717/peerj-cs.660

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…Hence, assemble models for streaming contexts are being developed to increase classification accuracy to address these issues [24][25][26]. Existing ensemble techniques generally improve the problem of prediction accuracy by training individual classifiers based on different sets of data examples and combining them to predict incoming instances using predefined weighting algorithms [24,27]. Yet, such strategies were ineffective in dealing with concept drift.…”

Section: Related Workmentioning

confidence: 99%

Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification

Rabiu¹,

Salim²,

Nasser³

et al. 2023

Computers, Materials &Amp; Continua

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Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products. Due to changes in data distribution, commonly referred to as concept drift, mining this data stream is a challenging problem for researchers. The majority of the existing drift detection techniques are based on classification errors, which have higher probabilities of false-positive or missed detections. To improve classification accuracy, there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams. This paper presents an adaptive unsupervised learning technique, an ensemble classifier based on drift detection for opinion mining and sentiment classification. To improve classification performance, this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data stream. Whenever a drift is detected, the proposed method builds and adds a new classifier to the ensemble. To add a new classifier, the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the ensemble. To this end, a weighting mechanism is used to calculate the weight of each classifier, which decides the contribution of each classifier in the final classification results. Several experiments were conducted on real-world datasets and the results were evaluated on the false positive rate, miss detection rate, and accuracy measures. The proposed method is also compared with the state-of-the-art methods, which include DDM, EDDM, and PageHinkley with support vector machine (SVM) and Naïve Bayes classifiers that are frequently used in concept drift detection studies. In all cases, the results show the efficiency of our proposed method.

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Section: Related Workmentioning

confidence: 99%

Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification

Rabiu¹,

Salim²,

Nasser³

et al. 2023

Computers, Materials &Amp; Continua

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“…Although their composition takes many forms, they are still quite valuable and representative after processed properly ( Xiao, Wei & Dong, 2016 ; Lycett, 2013 ). Current researches on online reviews mainly focus on sentiment analysis ( Kumar et al, 2021 ; Colón-Ruiz & Segura-Bedmar, 2020 ), user recommendations and purchase decisions ( Willemsen et al, 2015 ; Yang, Cheng & Tong, 2015 ), feature identification, usefulness ( Chatterjee, 2001 ; Majumder, Gupta & Paul, 2022 ) and their impact on product pricing and sales, etc . Jensen et al (2013) introduced persuasion models into the analysis of online reviews to explore how they affect consumers’ perceptions of products.…”

Section: Related Workmentioning

confidence: 99%

Scenario-feature identification from online reviews based on BERT

Xun-jiang¹,

Kang²

2023

PeerJ Computer Science

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Scenario endows a product with meanings. It has become the key to win the competition to design a product according to specific usage scene. Traditional scenario identification and product feature association methods have disadvantages such as subjectivity, high cost, coarse granularity, and limited scenario can be identified. In this regard, we propose a BERT-based scenario-feature identification model to effectively extract the information about users’ experience and usage scene from online reviews. First, the scenario-feature identification framework is proposed to depict the whole identification process. Then, the BERT-based scene-sentence recognition model is constructed. The Skip-gram and word vector similarity methods are used to construct the scene and feature lexicon. Finally, the triad is constructed through the analysis of scene-feature co-occurrence matrix, which realizes the association of scenario and product features. This proposed model is of great practical value for product developers to better understand customer’s requirements in specific scenarios. The experiments of scenario-feature identification from the reviews of Pacific Auto verifies the effectiveness of this method.

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“…In [51], the authors proposed a new framework for joint sentiment-topic modeling based on the Restricted Boltzmann Machine (RBM), a type of neural network. In [52], the authors proposed a probabilistic method to incorporate textual reviews and overall ratings, considering their natural connection for a joint sentiment-topic prediction. In [53], the authors proposed a hybrid topic modelbased method for aspect extraction and emotion categorization of reviews.…”

Section: Related Workmentioning

confidence: 99%

Weighted Joint Sentiment-Topic Model for Sentiment Analysis Compared to ALGA: Adaptive Lexicon Learning Using Genetic Algorithm

Osmani

Mohasefi

2022

Computational Intelligence and Neuroscience

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Latent Dirichlet Allocation (LDA) is an approach to unsupervised learning that aims to investigate the semantics among words in a document as well as the influence of a subject on a word. As an LDA-based model, Joint Sentiment-Topic (JST) examines the impact of topics and emotions on words. The emotion parameter is insufficient, and additional parameters may play valuable roles in achieving better performance. In this study, two new topic models, Weighted Joint Sentiment-Topic (WJST) and Weighted Joint Sentiment-Topic 1 (WJST1), have been presented to extend and improve JST through two new parameters that can generate a sentiment dictionary. In the proposed methods, each word in a document affects its neighbors, and different words in the document may be affected simultaneously by several neighbor words. Therefore, proposed models consider the effect of words on each other, which, from our view, is an important factor and can increase the performance of baseline methods. Regarding evaluation results, the new parameters have an immense effect on model accuracy. While not requiring labeled data, the proposed methods are more accurate than discriminative models such as SVM and logistic regression in accordance with evaluation results. The proposed methods are simple with a low number of parameters. While providing a broad perception of connections between different words in documents of a single collection (single-domain) or multiple collections (multidomain), the proposed methods have prepared solutions for two different situations (single-domain and multidomain). WJST is suitable for multidomain datasets, and WJST1 is a version of WJST which is suitable for single-domain datasets. While being able to detect emotion at the level of the document, the proposed models improve the evaluation outcomes of the baseline approaches. Thirteen datasets with different sizes have been used in implementations. In this study, perplexity, opinion mining at the level of the document, and topic_coherency are employed for assessment. Also, a statistical test called Friedman test is used to check whether the results of the proposed models are statistically different from the results of other algorithms. As can be seen from results, the accuracy of proposed methods is above 80% for most of the datasets. WJST1 achieves the highest accuracy on Movie dataset with 97 percent, and WJST achieves the highest accuracy on Electronic dataset with 86 percent. The proposed models obtain better results compared to Adaptive Lexicon learning using Genetic Algorithm (ALGA), which employs an evolutionary approach to make an emotion dictionary. Results show that the proposed methods perform better with different topic number settings, especially for WJST1 with 97% accuracy at |Z| = 5 on the Movie dataset.

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Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

Cited by 11 publications

References 24 publications

Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification

Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification

Scenario-feature identification from online reviews based on BERT

Weighted Joint Sentiment-Topic Model for Sentiment Analysis Compared to ALGA: Adaptive Lexicon Learning Using Genetic Algorithm

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