Cross-modal complementary network with hierarchical fusion for multimodal sentiment classification

Peng, Cheng; Zhang, Chunxia; Xue, Xiaojun; Gao, Jiameng; Liang, Hongjian; Niu, Zhendong

doi:10.26599/tst.2021.9010055

Cited by 26 publications

(12 citation statements)

References 36 publications

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“…Therefore, we will further improve CPCR method by adding more context factors and weights. Moreover, the historical POIs check-in data are often sensitive to travelers (Cai, Z., et al, 2021;Cai, Z., et al, 2018;Liu, H., et al, 2019;Kong, L., et al, 2021;Peng, C., et al, 2022;Vedadi, A., et al, 2021;Xu, Y., et al, 2017;Zheng, X., et al, 2020;. Therefore, privacy issue would be taken into consideration in our future research.…”

Section: Discussionmentioning

confidence: 99%

POIs Category Recommendation for Cultural Country Travel Enterprises Based on Check-In Information

Liu

Luhach

Lee

2022

Journal of Global Information Management

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With the ever-increasing popularity of traveling market, more and more people are willing to spend their time and enjoy life by visiting some Point Of Interests (POI) especially for the citizens living in city. Therefore, it is of practical and significant value for travel enterprises to recommend appropriate country POIs to target travelers. However, the massive POIs as well as their diversity place a heavy burden on the POIs recommendation decision-makings especially when the available historical traveler-POIs check-in data are very sparse. In view of this challenge, we put forward a novel cultural country POIs category recommendation method based on historical knowledge and experienced information, e.g., check-in time and POIs category, i.e., CPCR. At last, CPCR method is evaluated via experiments on WS-DREAM dataset.

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Section: Discussionmentioning

confidence: 99%

POIs Category Recommendation for Cultural Country Travel Enterprises Based on Check-In Information

Liu

Luhach

Lee

2022

Journal of Global Information Management

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“…This algorithm mainly distills neural network, cluster analysis, hidden semantic model, and matrix decomposition [16] to predict the score and recommendation of blank data. Whereas, dynamic changes in user preferences and practical problems of cold start [17][18][19][20][21][22][23] are arduous to figure out, resulting in certain deficiencies in the existing methods. Therefore, the recommendation system can not provide users with accurate personalized services (Cold start condition is a new form of problem [24][25][26], which is reflected between users and projects.).…”

Section: Collaborative Filtering Recommendationmentioning

confidence: 99%

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

Liu¹,

Wan²,

Yan³

2023

Computer Modeling in Engineering &Amp; Sciences

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With the popularity of 5G and the rapid development of mobile terminals, an endless stream of short video software exists. Browsing short-form mobile video in fragmented time has become the mainstream of user's life. Hence, designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements. Nevertheless, the explosive growth of data leads to the low efficiency of the algorithm, which fails to distill users' points of interest on one hand effectively. On the other hand, integrating user preferences and the content of items urgently intensify the requirements for platform recommendation. In this paper, we propose a collaborative filtering algorithm, integrating time context information and user context, which pours attention into expanding and discovering user interest. In the first place, we introduce the temporal context information into the typical collaborative filtering algorithm, and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos. There remains one more point. We also introduce the user situation into the traditional collaborative filtering recommendation algorithm, considering the context information of users in the generation recommendation stage, and weight the recommended short-form videos of candidates. At last, a diverse approach is used to generate a Top-K recommendation list for users. And through a case study, we illustrate the accuracy and diversity of the proposed method.

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“…Te method based on machine learning is to train a large number of labeled text data to obtain a pretraining model, so as to predict unknown text for sentiment classifcation. For example, Reference [14] proposed a hierarchical fusion cross-modal complementary network for multi-modal sentiment network analysis. Te feature extraction module from text and image was used to learn the attention features of text and image generated by the image-text generator to form a hierarchical fusion framework, which could fully integrate diferent modal features and accurately analyze the sentiment of text and image.…”

Section: Related Workmentioning

confidence: 99%

Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

Shan

2023

Mobile Information Systems

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Aiming at the serious colloquialism of social network texts and the sparse semantic features, this article proposes a CNN-BiGRU-based sentiment analysis method for social network texts in the big data environment. First, the dependency syntax tree is introduced to represent the dependency relationship between words to construct the word vector to represent the text. Then, sentiment features with different granularity are extracted by multiple convolution kernels of different sizes in a convolution neural network (CNN). These sentiment features are input into bidirectional gated recurrent unit (BiGRU) network for analysis to obtain deeper sentiment features. Finally, a certain number of neurons are discarded by the Dropout method, and sentiment types are classified by the Sigmoid activation function. The Weibo_senti_100k Weibo data set is used to demonstrate the proposed method. The results show that if the Dropout value is set to 0.25 and the Adam optimizer is selected, the analysis performance is the best. The accuracy, precision, recall, and AUC are about 94.09%, 95.13%, 92.87%, and 0.953, respectively, which has certain application value.

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Cross-modal complementary network with hierarchical fusion for multimodal sentiment classification

Cited by 26 publications

References 36 publications

POIs Category Recommendation for Cultural Country Travel Enterprises Based on Check-In Information

POIs Category Recommendation for Cultural Country Travel Enterprises Based on Check-In Information

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

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