Toward multi-label sentiment analysis: a transfer learning based approach

Tao, Jie; Fang, Xing

doi:10.1186/s40537-019-0278-0

Cited by 203 publications

(37 citation statements)

References 29 publications

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“…Cosine and Jaccard similarity techniques are the two text-based similarity approach which has been widely incorporated for finding similar text (Sohangir & Wang, 2017;Amer & Abdalla, 2020). But these approaches, when applied to question-based corpus for identifying similar question text, lead to the recommendation issues, as discussed in the following subsections.…”

Section: Issues In Similarity-based Recommendation Of Questionsmentioning

confidence: 99%

Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

Pal

Pramanik

Maity

et al. 2021

PeerJ Computer Science

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In an interactive online learning system (OLS), it is crucial for the learners to form the questions correctly in order to be provided or recommended appropriate learning materials. The incorrect question formation may lead the OLS to be confused, resulting in providing or recommending inappropriate study materials, which, in turn, affects the learning quality and experience and learner satisfaction. In this paper, we propose a novel method to assess the correctness of the learner's question in terms of syntax and semantics. Assessing the learner’s query precisely will improve the performance of the recommendation. A tri-gram language model is built, and trained and tested on corpora of 2,533 and 634 questions on Java, respectively, collected from books, blogs, websites, and university exam papers. The proposed method has exhibited 92% accuracy in identifying a question as correct or incorrect. Furthermore, in case the learner's input question is not correct, we propose an additional framework to guide the learner leading to a correct question that closely matches her intended question. For recommending correct questions, soft cosine based similarity is used. The proposed framework is tested on a group of learners' real-time questions and observed to accomplish 85% accuracy.

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Section: Issues In Similarity-based Recommendation Of Questionsmentioning

confidence: 99%

Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

Pal

Pramanik

Maity

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…They conducted a detailed empirical study of different multilabel classification methods for sentiment classification to compare the classification performances. In addition, the study [36] designed a more complex multilabel ABSA method that could predict one or multiple aspect-sentiment labels from the text. Also the study [37] promoted a Recurrent Neural Network (RNN) language model based on Long Short-Term Memory (LSTM) networks to implement multiclassification for texts sentiment.…”

Section: B Multiclassification Of Sentimentmentioning

confidence: 99%

A Multiclassification Model of Sentiment for E-Commerce Reviews

Zhang

Zhong

et al. 2020

IEEE Access

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Consumer reviews are important information that reflects the quality of E-commerce goods and services and their existing problems after shopping. Due to the possible differences in consumers' experiences with goods and service quality, consumer reviews can involve multiple-aspect expressions of emotions or opinions. This may result in attitudes expressed by a consumer in the same review sometimes having a variety of emotions. We introduce a sentiment multiclassification method based on a directed weighted model. The model represents the sentiment entity vocabulary as the sentiment nodes and represents the relation between nodes as the directed weighted link. The sentiment entity vocabulary is the entity with attributes, which can express sentiment meaning in related reviews. Directed weighted links represent the sentiment similarity between two nodes of entities with attributes and determined by the direct correlation calculation between them. The paths are all connected directed links from one node to another, which are composed of several nodes and links with close sentiment similarity. Then, we can establish a directed weighted model concerning the sentiments. Directed weighted links having similar sentiment relations with each other may constitute a directed weighted path. There are several directed weighted paths from a start node to the end nodes of the sentiment entity vocabulary in the directed weighted model. Each different path is a different sentiment expression, which represents a different sentiment type. The different sentiment classifications can be obtained through the restriction of path length. Experiments and analysis of the results show that the sentiment multiclassification model based on the directed weighted model proposed in this paper can classify the review sentiments according to different limited threshold rules. Comprehensive analysis indicates the classification results have good accuracy and high efficiency.

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“…XLNet is generalized because of utilizing a mechanism called permutation language modeling that captures the bidirectional context and integrates the idea of auto-regressive models with bidirectional context modeling. Thus, this paper utilizes the XLNet technique because it overcomes BERT's drawbacks and it also achieves better performance in many NLP tasks than most of other pre-training approaches [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Sentence-Level Aspect-Based Sentiment Analysis for Classifying Adverse Drug Reactions (ADRs) Using Hybrid Ontology-XLNet Transfer Learning

2021

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This paper presents a hybrid ontology-XLNet sentiment analysis classification approach for sentence-level aspects. The main objective of the proposed approach allows discovering user social data considering the extracted in-depth inference about sentiment depending on the context. Thus, in this paper, we investigate the contribution of utilizing the lexicalized ontology to improve the aspect-based sentiment analysis performance through extracting the indirect relationships in user social data. The XLNet model is utilized for extracting the neighboring contextual meaning and concatenating it with each embeddings word to produce a more comprehensive context and enhance feature extraction. In the proposed approach, Bidirectional Long Short Term Memory (Bi-LSTM) networks are used for classifying the aspects in online user reviews. Various experiments considering Adverse Drug Reactions (ADRs) discovery are conducted on six drug-related social data real-world datasets to evaluate the performance of the proposed approach using several measures. Obtained experimental results show that the proposed approach outperformed other tested state-of-the-art related approaches through improving feature extraction of unstructured social media text and accordingly improving the overall accuracy of sentiment classification. A significant accuracy of 98% and F-measure of 96.4% are achieved by the proposed ADRs aspect-based sentiment analysis approach.

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Toward multi-label sentiment analysis: a transfer learning based approach

Cited by 203 publications

References 29 publications

Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

A Multiclassification Model of Sentiment for E-Commerce Reviews

Sentence-Level Aspect-Based Sentiment Analysis for Classifying Adverse Drug Reactions (ADRs) Using Hybrid Ontology-XLNet Transfer Learning

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