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  • Open Access

    ARTICLE

    Learning Temporal User Features for Repost Prediction with Large Language Models

    Wu-Jiu Sun1, Xiao Fan Liu1,2,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4117-4136, 2025, DOI:10.32604/cmc.2025.059528 - 06 March 2025

    Abstract Predicting information dissemination on social media, specifically users’ reposting behavior, is crucial for applications such as advertising campaigns. Conventional methods use deep neural networks to make predictions based on features related to user topic interests and social preferences. However, these models frequently fail to account for the difficulties arising from limited training data and model size, which restrict their capacity to learn and capture the intricate patterns within microblogging data. To overcome this limitation, we introduce a novel model Adapt pre-trained Large Language model for Reposting Prediction (ALL-RP), which incorporates two key steps: (1)… More >

  • Open Access

    ARTICLE

    Aspect-Level Sentiment Analysis Based on Deep Learning

    Mengqi Zhang1, Jiazhao Chai2, Jianxiang Cao3, Jialing Ji3, Tong Yi4,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3743-3762, 2024, DOI:10.32604/cmc.2024.048486 - 26 March 2024

    Abstract In recent years, deep learning methods have developed rapidly and found application in many fields, including natural language processing. In the field of aspect-level sentiment analysis, deep learning methods can also greatly improve the performance of models. However, previous studies did not take into account the relationship between user feature extraction and contextual terms. To address this issue, we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method. To be specific, we design user comment feature extraction (UCFE) to distill salient features from users’ historical comments and transform them More >

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