| dc.description.abstract | Social media networks provide rich and diverse information, making opinion analysis and network volume analysis have become one of the methods to investigate and understand the market. The above method can provide information other than platform. It can helps to solve the problem that the customer group is restricted to a specific platform, Enable the platform to obtain more comprehensive data for analysis and decision-making. However, most social media are based on text narratives. How to effectively convert the results of public opinion analysis and text messages into numerical data, which will facilitate subsequent data analysis, is the subject of our research.
Our main research is to apply ＂singer & song network mentions＂ and ＂Aspect-based sentiment analysis＂ to ＂Hit Song prediction＂. In ＂singer & song network mentions＂ tasks, we focus on Chinese district singers and songs, and follow the social media ＂PTT＂ to pay attention to follow the network mentions of singers and songs. Since the content of a sentence may be commented on different targets, it is difficult to label the emotions of the model.
Therefore, In our Research, Further use the ＂Aspect-based Sentiment Classification＂ to predict sentiment polarity on discussions related to singers and songs. We use different embedding layer methods in ASC tasks, such as pre-trained Word Embedding, Character embedding, and BERT[5] to transfer words and character into vectors, and on these embedding layer methods, Huang et al.[11] Aspect-based Sentiment Classification method and Parikh et al.[17] natural language inference method are used for the Aspect-based Sentiment Classification tasks. Then use the pre-train Aspect sentiment model to analyze the social media comments, and add the analysis results to the platform’s on-demand information to predict the future play volume of songs.
In Hit Song prediction task, use neural network to design prediction model, Solve the high dimensional features data and learning the relationship between features through neural networks, This eliminates the need to rely on artificial experience and cumbersome statistical methods for feature processing in the past. The experimental results show that the addition of ＂network mention information＂ and ＂sentiment analysis＂ can improve hit song prediction performance. | en_US |