@inproceedings{cheng-etal-2021-using,
    title = "Using Valence and Arousal-infused {B}i-{LSTM} for Sentiment Analysis in Social Media Product Reviews",
    author = "Cheng, Yu-Ya  and
      Yeh, Wen-Chao  and
      Chen, Yan-Ming  and
      Chang, Yung-Chun",
    editor = "Lee, Lung-Hao  and
      Chang, Chia-Hui  and
      Chen, Kuan-Yu",
    booktitle = "Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)",
    month = oct,
    year = "2021",
    address = "Taoyuan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2021.rocling-1.27/",
    pages = "210--217",
    abstract = "With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user`s experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text."
}