Sensitive Information Topics-Based Sentiment Analysis Method for Big Data

Xu, Guixian; Yu, Zhuang; Zhan, Chen; Qiu, Xiaoyu; Yao, Haishen

doi:10.1109/access.2019.2927360

Cited by 15 publications

(7 citation statements)

References 21 publications

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“…The performance parameters used in this work are accuracy, sensitivity or recall, F-measure, and G-mean, to assess the effectiveness of the NLP and affective analysis approach. G-mean = √ TP rate × TN rate (7) where TP, TN, FP, and FN represent true positives, true negatives, false positives, and false negatives, respectively. The TP rate = TP/p and TN rate = TN/n, in which p represents the number of positive samples and n represents the number of negative samples.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

Rosa

Silva

et al. 2020

IEEE Access

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People use Online Social Networks (OSNs) to express their opinions and feelings about many topics. Depending on the nature of an event and its dissemination rate in OSNs, and considering specific regions, the users' behavior can drastically change over a specific period of time. In this context, this work aims to propose an event detection system at the early stages of an event based on changes in the users' behavior in an OSN. This system can detect an event of any subject, and thus, it can be used for different purposes. The proposed event detection system is composed of the following main modules: (1) determination of the user's location, (2) message extraction from an OSN, (3) topic identification using natural language processing (NLP) based on the Deep Belief Network (DBN), (4) the user behavior change analyzer in the OSN, and (5) affective analysis for emotion identification based on a tree-convolutional neural network (tree-CNN). In the case of public health, the early event detection is very relevant for the population and the authorities in order to be able take corrective actions. Hence, the new coronavirus disease (COVID-19) is used as a case study in this work. For performance validation, the modules related to the topic identification and affective analysis were compared with other similar solutions or implemented with other machine learning algorithms. In the performance assessment, the proposed event detection system achieved an accuracy higher than 0.90, while other similar methods reached accuracy values less than 0.74. Additionally, our proposed system was able to detect an event almost three days earlier than the other methods. Furthermore, the information provided by the system permits to understand the predominant characteristics of an event, such as keywords and emotion type of messages. INDEX TERMS Event detection, Online Social Networks, affective analysis, natural language processing, COVID-19.

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Section: Performance Evaluation Metricsmentioning

confidence: 99%

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

Rosa

Silva

et al. 2020

IEEE Access

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“…The method provided a rule-based emotion output mechanism. Xu et al 46 used a neural network model to integrate topic semantic information into the text representation and proposed a big data sentiment analysis method based on sensitive information topics. The sentiment analysis system established by Wang et al 47 can instantly and continuously analyze the sentiment of the Twitter users about elections.…”

Section: Related Workmentioning

confidence: 99%

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

Wang

Zhu

et al. 2021

International Journal of Distributed Sensor Networks

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“Social sensors” refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19” collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.

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“…Xu et al [3] proposed an analysis of the sentiment of big data by integrating semantic textual information and neural networks. This approach calculates the weight of individual words and creates a context-sensitive vector.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Optimization and Machine Learning for Sentiment Analysis

Chourasiya¹,

Rathod²

2021

IJOSCIENCE

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Sentiment analysis is called detecting emotions extracted from text features and is known as one of the most important parts of opinion extraction. Through this process, we can determine if a script is positive, negative or neutral. In this research, sentiment analysis is performed with textual data. A text feeling analyzer combines natural language processing (NLP) and machine learning techniques to assign weighted assessment scores to entities, subjects, subjects, and categories within a sentence or phrase. In expressing mood, the polarity of text reviews could be graded on a negative to positive scale using a learning algorithm. The current decade has seen significant developments in artificial intelligence, and the machine learning revolution has changed the entire AI industry. After all, machine learning techniques have become an integral part of any model in today's computing world. However, the ensemble to learning techniques is promise a high level of automation with the extraction of generalized rules for text and sentiment classification activities. This thesis aims to design and implement an optimized functionality matrix using to the ensemble learning for the sentiment classification and its applications.

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Sensitive Information Topics-Based Sentiment Analysis Method for Big Data

Cited by 15 publications

References 21 publications

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

Application of Optimization and Machine Learning for Sentiment Analysis

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