Abstract
Emotional tracking on time-varying virtual space communication aims to identify sentiments and opinions expressed in a piece of user-generated content. However, the existing research mainly focuses on the user’s single post, despite the fact that social network data are sequential. In this article, we propose a sentiment analysis model based on time series prediction in order to understand and master the chronological evolution of the user’s point of view. Specifically, with the help of a domain-knowledge-enhanced pre-trained encoder, the model embeds tokens for each moment in the text sequence. We then propose an attention-based temporal prediction model to extract rich timing information from historical posting records, which improves the prediction of the user’s current state and personalizes the analysis of user’s sentiment changes in social networks. The experiments show that the proposed model improves on four kinds of sentiment tasks and significantly outperforms the strong baseline.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference36 articles.
1. Temporal Sentiment Analysis of Socially Important Locations of Social Media Users;Ecemiş;Proceedings of the Third International Conference on Smart City Applications,2018
2. Time series forecasting using a hybrid ARIMA and neural network model
3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding;Kenton;Proceedings of the NAACL-HLT,2019
4. Thumbs up? Sentiment Classification using Machine Learning Techniques;Pang;Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002),2002
5. Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis;Poria;Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN),2016
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