Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

Abstract

With the rapid development of social media, danmaku video provides a platform for users to communicate online. To some extent, danmaku video provides emotional timing information and an innovative method to analyze video data. In the age of big data, studying the characteristics of danmaku and its emotional tendencies can not only help us understand the psychological characteristics of users but also feedback the effective information of users to video platforms, which can help the platforms optimize related short video recommendations so that it can provide a more accurate solution for the selection of audiences during video production. However, danmaku is different from traditional comments. Current emotion classification methods are only suitable for two-dimensional classification which are not suitable for danmaku emotion analysis. Aiming at the problems such as the colloquialism, diversity, spelling errors, structural non-linearity informal language on the Internet, diversity of social topics, and context dependency of emotion analysis of the danmaku data, this paper proposes an improved emotion analysis model based on Bi-LSTM model to classify the further four-dimensional emotions of Pleasure, Anger, Sorrow and Joy. Furthermore, we add tags such as comment time and user name to the danmaku information. Experimental results show that the improved model has higher Accuracy, Recall, Precision, and F1-Score under the same conditions compared with the CNN and SVM. The classification effect of improved model is close to the SOTA. Experimental results also show that the improved model can be effectively applied to the analysis of irregular danmaku emotion.

Prediction and Influencing Factors of Big Data on College Students’ Positive Psychological Quality under Mobile Wireless Network

“…With these data, the metric accuracy, precision, false rate, and processing time are comparatively analyzed by varying the observations and classi cations. In this comparative analysis, EAM-Bi-LSTM [16], EDM-DL [17], and MA-RP [25] methods are compared with proposed BPRM method.…”

“…Wang et al [16] introduced a danmaku emotion analysis method using the bidirectional long short-term memory (Bi-LSTM) approach.…”

Time-synchronic comments on video streaming website reveal core structures of audience engagement in movie viewing

“…Research on time-synchronic comments is done from different perspectives, namely pragmatics ( Zhou, 2015 ; Ruxin, 2018 ; Wang, 2021 ; Zhang and Cassany, 2021 ), communication and media studies ( Liu et al, 2016 ; Zhu, 2017 ; Zhang and Cassany, 2019 ; Yang, 2021 ; Wang, 2022 ), and computer science ( He et al, 2016 ; Sun et al, 2016 ; Wang et al, 2020 ; Zhang et al, 2020 ).…”

“…On the one hand, linguists are curious about the pragmatical functions and cross-cultural distinctions revealed by time-synchronic comments. Wang et al (2020) explored for example, how and why people are impolite in time-synchronic comments. The author manually collected 26,875 comments on 49 videos that contained impolite metapragmatic comments, and found seven impoliteness strategies.…”

“…For instance, Sun et al (2016) designed VideoForest , a system that can convert an input video with time-synchronic comment data into a tree-like visual summary of movie content. As for Wang et al (2020) , they utilized a Bidirectional Long Short-Term Memory (Bi-LSTM) model to classify four emotions – Pleasure, Anger, Sorrow, and Joy – in time-synchronic comments from the NBA (The National Basketball Association) videos available on Tencent Video.…”