An improved algorithm for sentiment analysis based on maximum entropy

Xie, Xin; Ge, Songlin; Hu, Fengping; Xie, Mingye; Jiang, Nan

doi:10.1007/s00500-017-2904-0

Cited by 69 publications

(31 citation statements)

References 20 publications

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“…The algorithms used in sentiment analysis are based on Naive Bayes (NB), Max Entropy (MaxEnt) and Support Vector Machines (SVM) (Altrabsheh et al 2013 ; Xie et al 2019 ). Sentiment analysis requires, identifying and extracting relevant information about participants’ subjective feelings, and experiences towards a product, event or a phenomenon.…”

Section: Methods and Samplingmentioning

confidence: 99%

Sentiment Analysis of Student Engagement with Lecture Recording

Nkomo

Daniel

2021

TechTrends

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Lecture recordings are useful learning resources that can support flexible, remote and distance learning, especially during the Covid-19 pandemic. This study explored students' experiences of engaging with recorded lectures and the extent to which such engagement contributes to enhanced learning. The research involved the deployment of a questionnaire to undergraduate and postgraduate students (n = 660) who had access to lecture recordings at a research intensive public university in New Zealand. Key findings revealed that students who accessed lecture recordings reported an enhanced learning experience. They stated that the availability of lecture recordings created an inclusive learning environment for students with learning disabilities and fostered flexible learning for those who would otherwise be unable to attend lectures either due to sickness or work commitments. Data also suggest that students utilised lecture recordings as supplementary learning resources rather than a substitute for regular lecture attendance. Our findings substantiated previous research studies and additionally demonstrated the various ways in which students engage with lecture recordings. Moreover, the use of sentiment analysis shows how multiple data can be triangulated to confirm or contest research findings. Finally, we believe this study adds to the voice that lecture recordings are vital resources that support students' learning.

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Section: Methods and Samplingmentioning

confidence: 99%

Sentiment Analysis of Student Engagement with Lecture Recording

Nkomo

Daniel

2021

TechTrends

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“…Regression refers to the process of classifying a specific data into two classes thus, ME has extend the regression to include classifying data with multi-classes. The key characteristic behind ME lies on the assumption that data instances are case specific where every individual variable has a specific value for each case (20). In this regard, ME will address each individual variable by giving a score to generate the prediction model.…”

Section: Classificationmentioning

confidence: 99%

A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis

AL-Jumaili

2020

Baghdad Sci.J

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Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discriminate the polarity of sentiments. This paper proposes a hybrid method of linguistic and statistical features along with classification methods for Arabic sentiment analysis. Linguistic features contains stemming and POS tagging, while statistical contains the TF-IDF. A benchmark dataset of Arabic tweets have been used in the experiments. In addition, three classifiers have been utilized including SVM, KNN and ME. Results showed that SVM has outperformed the other classifiers by obtaining an f-score of 72.15%. This indicates the usefulness of using SVM with the proposed hybrid features.

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“…Arunachalam et al [17] and Verma and Thakur [18] et al discussed common machine learning algorithms (such as Bayesian, LDA, and dynamic ontology classification) in text classification and emotional polarity information mining. Among them, the application of common machine learning algorithms in text sentiment analysis includes Bayesian [19], [20], LDA [21], [22], NB [23], SVM [24], [25], the maximum entropy method [26], and KNN [27]. Liu et al [28] provided a method for multiclass sentiment classification based on an improved one-vs-one (OVO) strategy, and the support vector machine (SVM) algorithm was proposed.…”

Section: Related Workmentioning

confidence: 99%

Variable Convolution and Pooling Convolutional Neural Network for Text Sentiment Classification

Dong

Tang

et al. 2020

IEEE Access

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With the popularity of the internet, the expression of emotions and methods of communication are becoming increasingly abundant, and most of these emotions are transmitted in text form. Text sentiment classification research mainly includes three methods based on sentiment dictionaries, machine learning and deep learning. In recent years, many deep learning-based works have used TextCNN (text convolution neural network) to extract text semantic information for text sentiment analysis. However, TextCNN only considers the length of the sentence when extracting semantic information. It ignores the semantic features between word vectors and only considers the maximum feature value of the feature image in the pooling layer without considering other information. Therefore, in this paper, we propose a convolutional neural network based on multiple convolutions and pooling for text sentiment classification (variable convolution and pooling convolution neural network, VCPCNN). There are three contributions in this paper. First, a multiconvolution and pooling neural network is proposed for the TextCNN network structure. Second, four convolution operations are introduced in the word embedding dimension or direction, which are helpful for mining the local features on the semantic dimensions of word vectors. Finally, average pooling is introduced in the pooling layer, which is beneficial for saving the important feature information of the extracted features. The verification test was carried out on four emotional datasets, including English emotional polarity, Chinese emotional polarity, Chinese subjective and objective emotion and Chinese multicategory. Our apporach is effective in that its result was up to 1.97% higher than that of the TextCNN network. INDEX TERMS Text sentiment classification, deep learning, CNN.

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An improved algorithm for sentiment analysis based on maximum entropy

Cited by 69 publications

References 20 publications

Sentiment Analysis of Student Engagement with Lecture Recording

Sentiment Analysis of Student Engagement with Lecture Recording

A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis

Variable Convolution and Pooling Convolutional Neural Network for Text Sentiment Classification

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