Sentiment Analysis of Danmaku Videos Based on Naïve Bayes and Sentiment Dictionary

Abstract

Danmaku video provides a platform for users to communicate online while watching videos. Danmaku is a live commenting function where the comments related to the video being screened are created by users and prominently shown in real-time on the video screen. These live comments contain complex and rich sentiments, reflecting users' instant opinions and feelings on video programs. In some sense, danmaku provides emotional timing information about video data, and it also offers an innovative mean to analyze video data. However, existing sentiment classification methods are not suitable for danmaku data analysis. To solve this problem, this paper constructs a danmaku sentiment dictionary and presents a new method using sentiment dictionary and Naïve Bayes for the sentiment analysis of danmaku reviews. The method is greatly helpful in supervising the overall emotional orientation of a danmaku video and predicting its popularity. Through the processes of extracting emotional information from a danmaku video, classifying sentiment and visualizing data, the time distribution of the seven sentiment dimensions can be obtained. In addition, a weight calculation can be conducted for classifying the sentiment polarity of danmaku reviews. Experimental results show that the proposed method has a significant effect on sentiment score and polarity detection.

A review on Video Classification with Methods, Findings, Performance, Challenges, Limitations and Future Work

“…Traditional methods are subdivided into following types: traditional machine learning and deep learning. The SVM is the classification tools generally used in video classification, a system that uses Naïve Bayes and Dictionary [8]. K means used in various ways for video classification.…”

“…This section demonstrates the most widely used method of video classification including their working technique, application advantages and disadvantages. A way to identify video using Naïve Bayes and dictionary for the video classification [8]. If the statement of independent predictors is valid, a classifier from Naive Bayes functions works better than other models.…”

“…Violence detection from video of real time game [7] -2020 Video Scene classification [13] -2020 Event prediction [9] -2020 Animation movie video classification [8]…”

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

“…Research on danmaku has also a high commercial value. Studying the characteristics of danmaku and its emotional tendency has great practical value [5]. It feedbacks the effective information of users to video platforms, which can help the platforms improve live streaming technologies and optimize short video recommendations so that it can provide a more accurate solution for the selection of audiences during short video production.…”

“…the current time. The input is the cell state at the previous time 1 t C − and the memory gate ti , forgetting gate t f and temporary cell state ~t C calculated by (3)-(5). The cell state output at the current time t C is obtained by the following formula.…”