Sentiment recognition of online course reviews using multi-swarm optimization-based selected features

Li, Zhi; Liu, Sanya; Liu, Lin; Sun, Jianwen; Peng, Xian; Wang, Tai

doi:10.1016/j.neucom.2015.12.036

Cited by 42 publications

(20 citation statements)

References 19 publications

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“…In other text classification applications of spam filtering, DF is applied to the hybrid method (HBM) feature selection technique. It combines document frequency information and term frequency information [19]. This technique aims to solve the drawback in a single application of document frequency.…”

Section: Related Workmentioning

confidence: 99%

The Evaluation of Accuracy Performance in an Enhanced Embedded Feature Selection for Unstructured Text Classification

Nafis

Awang

2020

eijs

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Text documents are unstructured and high dimensional. Effective feature selection is required to select the most important and significant feature from the sparse feature space. Thus, this paper proposed an embedded feature selection technique based on Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for unstructured and high dimensional text classificationhis technique has the ability to measure the feature’s importance in a high-dimensional text document. In addition, it aims to increase the efficiency of the feature selection. Hence, obtaining a promising text classification accuracy. TF-IDF act as a filter approach which measures features importance of the text documents at the first stage. SVM-RFE utilized a backward feature elimination scheme to recursively remove insignificant features from the filtered feature subsets at the second stage. This research executes sets of experiments using a text document retrieved from a benchmark repository comprising a collection of Twitter posts. Pre-processing processes are applied to extract relevant features. After that, the pre-processed features are divided into training and testing datasets. Next, feature selection is implemented on the training dataset by calculating the TF-IDF score for each feature. SVM-RFE is applied for feature ranking as the next feature selection step. Only top-rank features will be selected for text classification using the SVM classifier. Based on the experiments, it shows that the proposed technique able to achieve 98% accuracy that outperformed other existing techniques. In conclusion, the proposed technique able to select the significant features in the unstructured and high dimensional text document.

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Section: Related Workmentioning

confidence: 99%

The Evaluation of Accuracy Performance in an Enhanced Embedded Feature Selection for Unstructured Text Classification

Nafis

Awang

2020

eijs

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“…Liu et al, [15] proposed a feature selection approach using the multi-swarm PSO for sentiment analysis to reduce redundancy of text features and improve the classification accuracy. Akhtar et al, [16] developed multi-objective optimization for aspect based sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

Anuprathibha*¹,

Selvi²

2019

IJRTE

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Twitter sentiment analysis is a vital concept in determining the public opinions about products, services, events or personality. Analyzing the medical tweets on a specific topic can provide immense benefits in medical industry. However, the medical tweets require efficient feature selection approach to produce significantly accurate results. Penguin search optimization algorithm (PeSOA) has the ability to resolve NP-hard problems. This paper aims at developing an automated opinion mining framework by modeling the feature selection problem as NP-hard optimization problem and using PeSOA based feature selection approach to solve it. Initially, the medical tweets based on cancer and drugs keywords are extracted and pre-processed to filter the relevant informative tweets. Then the features are extracted based on the Natural Language Processing (NLP) concepts and the optimal features are selected using PeSOA whose results are fed as input to three baseline classifiers to achieve optimal and accurate sentiment classification. The experimental results obtained through MATLAB simulations on cancer and drug tweets using k-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) indicate that the proposed PeSOA feature selection based tweet opinion mining has improved the classification performance significantly. It shows that the PeSOA feature selection with the SVM classifier provides superior sentiment classification than the other classifiers

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“…The results demonstrated that linguistic features boost performance [11]. For the prediction of sentiment, Liu et al proposed a feature selection method based on multi-swarm optimization to recognize the sentiment of online course reviews [12]. To understand learner performance, Tucker et al quantified the sentiment of textual data expressed by students using NLP techniques to compute the sentiment of each word.…”

Section: Nlp In Mooc Researchmentioning

confidence: 99%

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

et al. 2017

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Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.

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Sentiment recognition of online course reviews using multi-swarm optimization-based selected features

Cited by 42 publications

References 19 publications

The Evaluation of Accuracy Performance in an Enhanced Embedded Feature Selection for Unstructured Text Classification

The Evaluation of Accuracy Performance in an Enhanced Embedded Feature Selection for Unstructured Text Classification

Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

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