Abstract
As social media has become a ubiquitous part of daily life, researchers made a great progress in identifying the emotion in user-generated texts. However, it is a challenging task as people express their emotion in explicit and implicit ways. This paper focuses on the problem of identifying sentiments from implicit sentences which contain no emotional word or phrase. Most of the existing sentiment classification models cannot identify the sentiments accurately since they usually focus on extracting features from grammatical information without taking contextual information into account. In this paper, we argue that the contextual information is the key to identify sentiments in implicit sentences. Moreover, multiple features extracting from different aspects should be taken into account to improve sentiment identification. This paper proposes a multi-feature neural network model considering three aspects: contextual information, syntactic information and semantic information. To better get the semantic information of the sentence, we propose an attention mechanism based on contextual affective space. The experimental results on the SMP2019-ECISA dataset demonstrate that our model outperforms the previous systems and strong neural baselines.
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The datasets used in this study are obtained from SMP2019 (a top academic conference on social media processing in China). The link of the dataset is: http://biendata.com/competition/smpecisa2019/.
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Funding
This work was partially sponsored by the Natural Science Foundation of Zhejiang (Grants LY20F020007), and the Ningbo Science Technology Plan projects (Grants 2020Z082, 2021S091), and the K.C. Wong Magna Fund in Ningbo University.
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All authors contributed to the study. Yin Zhuang helped in programming and original draft preparation; Zhen Liu was involved in methodology and supervision; Ting-Ting Liu and Chih-Chieh Hung reviewed and edited the manuscript; Yan-Jie Chai collected the data.
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Zhuang, Y., Liu, Z., Liu, TT. et al. Implicit sentiment analysis based on multi-feature neural network model. Soft Comput 26, 635–644 (2022). https://doi.org/10.1007/s00500-021-06486-7
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DOI: https://doi.org/10.1007/s00500-021-06486-7