Integrating BERT Embeddings and BiLSTM for Emotion Analysis of Dialogue

Gou, Zhinan; Yan, Li

doi:10.1155/2023/6618452

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…This approach obtains an accuracy of 91.79%. Z. Gou et al [17] focuses on emotional classification using sentiment analysis with the BERT and BiLSTM network combination. It achieved an accuracy of 85.44%.…”

Section: Literature Reviewmentioning

confidence: 99%

DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media

Hossain,

Faruqui,

Mahmud

et al. 2023

Applied Sciences

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SocialMedia Marketing (SMM) has become a mainstream promotional scheme. Almost every business promotes itself through social media, and an educational institution is no different. The users’ responses to social media posts are crucial to a successful promotional campaign. An adverse reaction leaves a long-term negative impact on the audience, and the conversion rate falls. This is why selecting the content to share on social media is one of the most effective decisions behind the success of a campaign. This paper proposes a Data-Driven Promotional Management System (DPMS) for universities to guide the selection of appropriate content to promote on social media, which is more likely to obtain positive user reactions. The main objective of DPMS is to make effective decisions for Social Media Marketing (SMM). The novel DPMS uses a well-engineered and optimized BiLSTM network, classifying users’ sentiments about different university divisions, with a stunning accuracy of 98.66%. The average precision, recall, specificity, and F1-score of the DPMS are 98.12%, 98.24%, 99.39%, and 98.18%, respectively. This innovative Promotional Management System (PMS) increases the positive impression by 68.75%, reduces the adverse reaction by 31.25%, and increases the conversion rate by 18%. In a nutshell, the proposed DPMS is the first promotional management system for universities. It demonstrates significant potential for improving the brand value of universities and for increasing the intake rate.

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Section: Literature Reviewmentioning

confidence: 99%

DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media

Hossain,

Faruqui,

Mahmud

et al. 2023

Applied Sciences

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“…With the emergence of Bidirectional Encoder Representation from Transformers (BERT) [50], BERT has been introduced for textual features in emotion analysis and has achieved good results. Gou et al [51] generated word-level and sentence-level vectors for text using BERT, inserted the word-level feature into BiLSTM for processing, and connected the output of BiLSTM with sentence-level features for emotion analysis of dialogue. Their method significantly outperformed the baselines.…”

Section: Textual Emotion Classificationmentioning

confidence: 99%

Uni2Mul: A Conformer-Based Multimodal Emotion Classification Model by Considering Unimodal Expression Differences with Multi-Task Learning

Zhang,

Liu,

Jia

2023

Applied Sciences

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Multimodal emotion classification (MEC) has been extensively studied in human–computer interaction, healthcare, and other domains. Previous MEC research has utilized identical multimodal annotations (IMAs) to train unimodal models, hindering the learning of effective unimodal representations due to differences between unimodal expressions and multimodal perceptions. Additionally, most MEC fusion techniques fail to consider the unimodal–multimodal inconsistencies. This study addresses two important issues in MEC: learning satisfactory unimodal representations of emotion and accounting for unimodal–multimodal inconsistencies during the fusion process. To tackle these challenges, the authors propose the Two-Stage Conformer-based MEC model (Uni2Mul) with two key innovations: (1) in stage one, unimodal models are trained using independent unimodal annotations (IUAs) to optimize unimodal emotion representations; (2) in stage two, a Conformer-based architecture is employed to fuse the unimodal representations learned in stage one and predict IMAs, accounting for unimodal–multimodal differences. The proposed model is evaluated on the CH-SIMS dataset. The experimental results demonstrate that Uni2Mul outperforms baseline models. This study makes two key contributions: (1) the use of IUAs improves unimodal learning; (2) the two-stage approach addresses unimodal–multimodal inconsistencies during Conformer-based fusion. Uni2Mul advances MEC by enhancing unimodal representation learning and Conformer-based fusion.

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