Microblog sentiment analysis using social and topic context

Zou, Xiaomei; Yang, Jing; Zhang, Jianpei

doi:10.1371/journal.pone.0191163

Cited by 39 publications

(31 citation statements)

References 48 publications

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“…Sentimental analysis involves various research fields such as product recommendation [22], flight service [17] and opinion mining [23]. The data used in sentiment analysis are collected from online networks such as micro-blogs [24] and health forums [25]. The methods for sentiment analysis are roughly divided into the pattern-and machine learning-based approaches.…”

Section: A Sentimental Analysis In Social Mediamentioning

confidence: 99%

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Lin

Zheng

2020

IEEE Access

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Discovering more time-effective and a wider range of adverse drug reactions (ADRs) from social texts related to feelings concerning taking medication has recently received significant interest in pharmacovigilance research. Recognizing the posts that include ADRs is an important step for detecting ADRs from social texts. The existing systems show the unsatisfactory performance due to the insufficient expression of emotions and the inadequacy of information expression in short social texts. Although these systems exploit emotional features to improve the performance of their methods, the representation of wordlevel emotional scores is insufficient for emotional expression. Moreover, most of the systems make less use of medical knowledge to enhance the detection of the potential relationship between drugs and adverse reactions in posts. Therefore, enough expression of emotion and medical knowledge in sparse medical social texts may be explored to improve system performance. This paper proposed an effective method integrating sufficient emotional expression and medical knowledge to detect ADRs from medical tweets. First, the proposed method utilized sentence-level emotional context and word-level emotional score to learn sufficient emotional information for distinguishing between ADR and non-ADR tweets. Furthermore, a co-occurrence dictionary of each drug and its relevant ADRs was constructed by means of a medical resource (MedDRA) and drug site (www.drug.com) to help the proposed model focus on posts containing drugs and ADRs. Finally, a convolutional neural network (CNN) model on the basis of bidirectional encoder representations from transformers (BERT) performed the classification task. The proposed model achieved better overall performance than the other existing methods on two Twitter datasets (F1-scores of 72.64% and 64.98% on PSB2016 and SMM4H, respectively). INDEX TERMS Adverse drug reaction, medical knowledge, emotional context, co-occurrence dictionary, social text.

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Section: A Sentimental Analysis In Social Mediamentioning

confidence: 99%

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Lin

Zheng

2020

IEEE Access

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“…Naïve Bayes, maximum entropy, and support vector machine algorithms along with ensemble methodology, namely, weighted combination, meta-classifier combination, and the fixed combination, is considered a good approach in dealing with sentiment classification of microblogs such as Twitter [41][42][43][44][45][46][47]. Sentiment analysis can be handled in two ways; lexicon-based approach and machine learning.…”

Section: Sentiment Analysis and Lexicon Based Svm Classificationmentioning

confidence: 99%

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

Shafqat

Byun

2019

Sustainability

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With rapid advancements in internet applications, the growth rate of recommendation systems for tourists has skyrocketed. This has generated an enormous amount of travel-based data in the form of reviews, blogs, and ratings. However, most recommendation systems only recommend the top-rated places. Along with the top-ranked places, we aim to discover places that are often ignored by tourists owing to lack of promotion or effective advertising, referred to as under-emphasized locations. In this study, we use all relevant data, such as travel blogs, ratings, and reviews, in order to obtain optimal recommendations. We also aim to discover the latent factors that need to be addressed, such as food, cleanliness, and opening hours, and recommend a tourist place based on user history data. In this study, we propose a cross mapping table approach based on the location’s popularity, ratings, latent topics, and sentiments. An objective function for recommendation optimization is formulated based on these mappings. The baseline algorithms are latent Dirichlet allocation (LDA) and support vector machine (SVM). Our results show that the combined features of LDA, SVM, ratings, and cross mappings are conducive to enhanced performance. The main motivation of this study was to help tourist industries to direct more attention towards designing effective promotional activities for under-emphasized locations.

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“…Examples were target detection and microcalcifications detection and classification .RVM used a regression context and classification framework. Zou et al (2018) have proposed a new method combining social context and topic context to analyze microblog sentiment. Here structure similarity matrix built by introducing structure similarity context into social contexts and topic context matrix built by topic context.…”

Section: Related Workmentioning

confidence: 99%

A Novel Sentimental Analysis using Optimized Relevance Vector Machine Classifier

Saranya¹,

Gunavathi²,

2018

IJET

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Sentimental analysis is the process of identifying the human’s thoughts or feelings. So Many methods have been developed for the sentimental analysis. Machine learning is one of the widely used approaches towards sentiment classification. In this work, Sentimental analysis is done by using Relevance Vector Machine Classifier with Cuckoo Search Optimization. Here Relevance Vector Machine Classifier (RVMC) is combined with Cuckoo Search Optimization (CSO) for better accuracy and performance. Experiment is made with movie and twitter datasets. Accuracy, precision and recall of all other techniques are evaluated. Here the comparison is made among other algorithms. The result shows that RVMC-CSO algorithm gives accuracy and good performance than other algorithm like SVM, ELM and RVM.

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Microblog sentiment analysis using social and topic context

Cited by 39 publications

References 48 publications

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

A Novel Sentimental Analysis using Optimized Relevance Vector Machine Classifier

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