E-word of mouth sentiment analysis for user behavior studies

Li, Hui; Chen, Qi; Zhong, Zhaoman; Gong, Rongrong; Han, Guokai

doi:10.1016/j.ipm.2021.102784

Cited by 77 publications

(28 citation statements)

References 37 publications

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“…First, a new initialization method is used to initialize the node labels, and a new node similarity calculation method is defined in combination with a certain similarity between direct friends and indirect friends in social networks. LI et al [16] introduced social cognitive theory to infer and extract sentiment concepts for each sentiment review, using three machine learning algorithms to analyze sentiment. Verma et al [17] proposes a semi-supervised community detection algorithm inspired by genetic genealogy and based on a label propagation algorithm to detect communities in a network by considering the spread of influence from different community centers identified in the network.…”

Section: Community Discovery Algorithms Considering Overlapping Nodesmentioning

confidence: 99%

A novel overlapping community detection algorithm in smart city combining interest topic and local density

CHEN¹,

Jiang²,

HOU³

et al. 2022

Preprint

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It is very important to use ontology user portraits to describe the semantic structure interests of users and to study the similarity relations among user portraits for discovering overlapping interest communities in the process of building smart cities. The user interest’s hierarchical characteristics can produce different similarity relationships, forming interest clusters. The current paper proposes an overlapping community identification method based on hierarchical characteristics of user interest and local density is proposed, which can effectively promote the construction of smart cities. Firstly, a user hierarchical interest model based on ontology knowledge base was constructed to determine the users’ multi-granularity topic similarity. Then, a heterogeneous hypergraph was constructed by using multi-granularity topic similarity and user following similarity to describe the interest network. Using the peak mechanism of interest density, the community detection method was applied to identify the communities of interest. The real performance of the algorithm on multiple networks was verified by experiments. Experimental results showed the superiority of the presented algorithm over the classical overlapping community identification approach in terms of precision and recall rate.

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Section: Community Discovery Algorithms Considering Overlapping Nodesmentioning

confidence: 99%

A novel overlapping community detection algorithm in smart city combining interest topic and local density

CHEN¹,

Jiang²,

HOU³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Li H et al [18] have used machine learning classifiers NB, KNN, and SVM to assess 4300 emotions published on dating websites. They use a lexicon as well as a machine learning approach to analyze individual behavior for sentiment analysis.…”

Section: Review Of Machine Learning Techniques In User Opinion Analysismentioning

confidence: 99%

“…It is calculated by equation(17) Recall specifies the proportion of true positive rate of COVID tweets samples. It is calculated by equation(18). The F1 score is evaluated from both Recall and Precision.…”

mentioning

confidence: 99%

Classification of Twitter COVID Tweets Using Deep CNN-SLSTM Technique

2022

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A wide range of industries, including company management, travel and tourism management, education management (E-Learning), the medical field, government agencies, telecommunications management, and private firms, can benefit from user opinion analysis. Many classifiers for deep learning and machine learning are being created to automate sentiment analysis. For sentiment analysis, real-time tweets are initially gathered from various social media platforms like Twitter and Facebook as well as numerous review websites like Amazon and Flip-kart. In this research, a real-time dataset is created using the Twitter API and is used to gather tweets about COVIDbased keywords between

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“…While prior research has focused on persona perceptions [ 100 ] as the explanation for why personas fail, the importance of organizational factors concerning successful persona implementation is often overlooked. Yet, organizational factors tend to play a central role in creating better information systems and products [ 12 , 12 , 21 , 59 ]. A crucial observation in this regard is that IS and HCI have “shared concerns” [ 116 ] (p. 397) in terms of developing systems that have real-world value and impact – yet, these two disciplines often fail to share literature, theory, and findings; an effort that would benefit both fields [ 116 ].…”

Section: Introductionmentioning

confidence: 99%

Persona preparedness: a survey instrument for measuring the organizational readiness for deploying personas

et al. 2022

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User-centric design within organizations is crucial for developing information technology that offers optimal usability and user experience. Personas are a central user-centered design technique that puts people before technology and helps decision makers understand the needs and wants of the end-user segments of their products, systems, and services. However, it is not clear how ready organizations are to adopt persona thinking. To address these concerns, we develop and validate the Persona Readiness Scale (PRS), a survey instrument to measure organizational readiness for personas. After a 12-person qualitative pilot study, the PRS was administered to 372 professionals across different industries to examine its reliability and validity, including 125 for exploratory factor analysis and 247 for confirmatory factor analysis. The confirmatory factor analysis indicated a good fit with five dimensions: Culture readiness, Knowledge readiness, Data and systems readiness, Capability readiness, and Goal readiness. Higher persona readiness is positively associated with the respondents’ evaluations of successful persona projects. Organizations can apply the resulting 18-item scale to identify areas of improvement before initiating costly persona projects towards the overarching goal of user-centric product development. Located at the cross-section of information systems and human–computer interaction, our research provides a valuable instrument for organizations wanting to leverage personas towards more user-centric and empathetic decision making about users.

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E-word of mouth sentiment analysis for user behavior studies

Cited by 77 publications

References 37 publications

A novel overlapping community detection algorithm in smart city combining interest topic and local density

A novel overlapping community detection algorithm in smart city combining interest topic and local density

Classification of Twitter COVID Tweets Using Deep CNN-SLSTM Technique

Persona preparedness: a survey instrument for measuring the organizational readiness for deploying personas

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