A hybrid model for opinion mining based on domain sentiment dictionary

Cai, Yi; Yang, Kai; Huang, Dongping; Zhou, Zikai; Xue, Lei; Xie, Haoran; Wong, Tak Lam

doi:10.1007/s13042-017-0757-6

Cited by 44 publications

(13 citation statements)

References 23 publications

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“…We can see that the model with topic-based messages performs better than the model without topic-based messages. This finding (see Table 8) supports Huang et al [74] who noted that sentimental messages from annual reports (i.e., financial narratives) provide much denser and detailed information beyond just financial messages [75][76][77][78]. To avoid the outcome just happening by coincidence, we take the introduced model as a benchmark and further compare it with the other three AI-based algorithms: relevant vector machine (RVM), extreme learning machine (ELM), and back propagation neural network (BPNN).…”

Section: The Resultssupporting

confidence: 80%

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Huang

Lin

Hsu

2021

Axioms

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Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.

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Section: The Resultssupporting

confidence: 80%

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Huang

Lin

Hsu

2021

Axioms

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“…The hyper parameter further develops SVM and irregular woods model precision. The creator [6] found that feeling examination is useful in an assortment of disciplines, including administration, security, and others, where it very well might be used to execute different undertakings at different levels. It's a decent method to sort out how precise feeling examination AI strategies are.…”

Section: Literature Surveymentioning

confidence: 99%

“…The performance of four machine learning methods, including logistic regression, SVM, NB and Multilayer Perceptron (MLP) Classifier is evaluated. Sentiment analysis procedures are algorithms that have been trained to detect sentiment polarity [4][5][6] that can recognize sentiments in texts, sentences, or words automatically. A wide range of sentiment analysis procedure algorithm are available to address the various types of texts.…”

Section: Suggested Workmentioning

confidence: 99%

A Proportional Sentiment Analysis of MOOCs Course Reviews Using Supervised Learning Algorithms

Singh¹,

Kumar²,

Bhushan³

et al. 2021

ISI

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When anyone is looking to enroll for a freely available online course so the first and famous name comes in front of the searcher is MOOC courses. So here in this article our focus is to collect the comments by enrolled users for the specified MOOC course and apply sentiment analysis over that data. The significance of our article is to introduce a proficient sentiment analysis algorithm with high perceptive execution in MOOC courses, by seeking after the standards of gathering various supervised learning methods where the performance of various supervised machine learning algorithms in performing sentiment analysis of MOOC data. Some research questions have been addressed on sentiment analysis of MOOC data. For the assessment task, we have investigated a large no of MOOC courses, with the different Supervised Learning methods and calculated accuracy of the data by using parameters such as Precision, Recall and F1 Score. From the results we can conclude that when the bigram model was applied to the logistic regression, the Multilayer Perceptron (MLP) overcomes the accuracy by other algorithms as SVM, Naive Bayes and achieved an accuracy of 92.44 percent. To determine the sentiment polarity of a sentence, the suggested method use term frequency (No of Positive, Negative terms in the text) to calculate the sentiment polarity of the text. We use a logistic regression Function to predict the sentiment classification accuracy of positive and negative comments from the data.

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“…Bermudezgonzalez et al [1] proposed building a comprehensive Spanish sentiment repository for subjective analysis of emotions. Cai et al [2] solved the polysemy of emotional words by constructing a sentiment dictionary based on a specific domain. It is experimentally confirmed that the accuracy of using two superimposed classifiers, Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT) is better than that of a single model.…”

Section: Related Workmentioning

confidence: 99%

Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm

2020

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Integrated learning can be used to combine weak classifiers in order to improve the effect of emotional classification. Existing methods of emotional classification on micro-blogs seldom consider utilizing integrated learning. Personality can significantly influence user expressions but is seldom accounted for in emotional classification. In this study, a micro-blog emotion classification method is proposed based on a personality and bagging algorithm (PBAL). Introduce text personality analysis and use rule-based personality classification methods to divide five personality types. The micro-blog text is first classified using five personality basic emotion classifiers and a general emotion classifier. A long short-term memory language model is then used to train an emotion classifier for each set, which are then integrated together. Experimental results show that compared with traditional sentiment classifiers, PBAL has higher accuracy and recall. The F value has increased by 9%.

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A hybrid model for opinion mining based on domain sentiment dictionary

Cited by 44 publications

References 23 publications

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

A Proportional Sentiment Analysis of MOOCs Course Reviews Using Supervised Learning Algorithms

Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm

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