Improved Feature Representation Using Collaborative Network for Cross-Domain Sentiment Analysis

Gunasekar, M.; Thilagamani, S.

doi:10.5755/j01.itc.52.1.32119

Cited by 7 publications

(2 citation statements)

References 17 publications

(21 reference statements)

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“…Recommender systems are based on historical interaction data, mining user preferences and then recommending them to users [30,31]. Most of the early recommendation methods used user-personalized set matching based on collaborative filtering matching methods [32][33][34]. However, such methods suffer from the problem of the scarcity of data samples in cold-start situations.…”

Section: Graph Recommendationmentioning

confidence: 99%

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

Qu,

Zhang,

Liang

et al. 2023

Electronics

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The grid terminal deploys numerous types of communication equipment for the digital construction of the smart grid. Once communication equipment failure occurs, it might jeopardize the safety of the power grid. The massive amount of communication equipment leads to a dramatic increase in fault research and judgment data, making it difficult to locate fault information in equipment maintenance. Therefore, this paper designs a knowledge-graph-driven method for intelligent decision making on power communication equipment faults. The method consists of two parts: power knowledge extraction and user intent multi-feature learning recommendation. The power knowledge extraction model utilizes a multi-layer bidirectional encoder to capture the global features of the sentence and then characterizes the deep local semantics of the sentence through a convolutional pooling layer, which achieves the joint extraction and visual display of the fault entity relations. The user intent multi-feature learning recommendation model uses a graph convolutional neural network to aggregate the higher-order neighborhood information of faulty entities and then the cross-compression matrix to solve the feature interaction degree of the user and graph, which achieves accurate prediction of fault retrieval. The experimental results show that the method is optimal in knowledge extraction compared to classical models such as BERT-CRF, in which the F1 value reaches 81.7%, which can effectively extract fault knowledge. User intent multi-feature learning recommendation works best, with an F1 value of 87%. Compared with the classical models such as CKAN and KGCN, it is improved by 5%~11%, which can effectively solve the problem of insufficient mining of user retrieval intent. This method realizes accurate retrieval and personalized recommendation of fault information of electric power communication equipment.

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Section: Graph Recommendationmentioning

confidence: 99%

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

Qu,

Zhang,

Liang

et al. 2023

Electronics

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“…The dialogue surrounding warfare [2], conflict, protest [3] and the pursuit of peace is of particular note, as it reflects the intricate web of human emotions both positive (empathy, support) and negative (anger, hate [4,5]) that are inextricably linked to these profound issues. The expression of sentiments [6,7] on this platform transcends the boundaries of ephemeral commentary and can include fakes or disinformation [8]. It serves as a barometer for the prevailing societal attitudes towards the most pressing issues of war and peace.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Hinglish Tweet Dataset for Deep Pragmatic Analysis

Pratibha,

Kaur,

Khurana

et al. 2024

Data

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Wars, conflicts, and peace efforts have become inherent characteristics of regions, and understanding the prevailing sentiments related to these issues is crucial for finding long-lasting solutions. Twitter/`X’, with its vast user base and real-time nature, provides a valuable source to assess the raw emotions and opinions of people regarding war, conflict, and peace. This paper focuses on collecting and curating hinglish tweets specifically related to wars, conflicts, and associated taxonomy. The creation of said dataset addresses the existing gap in contemporary literature, which lacks comprehensive datasets capturing the emotions and sentiments expressed by individuals regarding wars, conflicts, and peace efforts. This dataset holds significant value and application in deep pragmatic analysis as it enables future researchers to identify the flow of sentiments, analyze the information architecture surrounding war, conflict, and peace effects, and delve into the associated psychology in this context. To ensure the dataset’s quality and relevance, a meticulous selection process was employed, resulting in the inclusion of explanable 500 carefully chosen search filters. The dataset currently has 10,040 tweets that have been validated with the help of human expert to make sure they are correct and accurate.

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