Leveraging Affective Hashtags for Ranking Music Recommendations
Leveraging Affective Hashtags for Ranking Music Recommendations
Project Title: | Leveraging Affective Hashtags for Ranking Music Recommendations. |
Implementation: | Java,MYSQL |
Project Cost: (In Indian Rupees) | Rs.3000/ |
Project Buy Link: | Buy Link |
IEEE BASE PAPER ABSTRACT:
Mood and emotion play an important role when it comes to choosing musical tracks to listen to. In the field of music information retrieval and recommendation, emotion is considered contextual information that is hard to capture, albeit highly influential. In this study, we analyze the connection between user’s emotional states and their musical choices. Particularly, we perform a largescale study based on two data sets containing 560,000 and 90,000 #nowplaying tweets, respectively. We extract affective contextual information from hashtags contained in these tweets by applying an unsupervised sentiment dictionary approach. Subsequently, we utilize a state-of-the-art network embedding method to learn latent feature representations of users, tracks and hashtags. Based on both the affective information and the latent features, a set of eight ranking methods is proposed. We find that relying on a ranking approach that incorporates the latent representations of users and tracks allows for capturing a user’s general musical preferences well (regardless of used hashtags or affective information). However, for capturing context-specific preferences (a more complex and personal ranking task), we find that ranking strategies that rely on affective information and that leverage hashtags as context information outperform the other ranking strategies.
PROJECT OUTPUT VIDEO:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium i3 Processor
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
SOFTWARE REQUIREMENTS:
- Operating system : Windows 10/11.
- Coding Language : JAVA.
- Frontend : JSP, HTML, CSS, JavaScript.
- IDE Tool : Netbeans 8.2
- Database : MYSQL
REFERENCE:
Eva Zangerle , Chih-Ming Chen, Ming-Feng Tsai , and Yi-Hsuan Yang, Senior Member, IEEE, “Leveraging Affective Hashtags for Ranking Music Recommendations”, IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. 12, NO. 1, JANUARY-MARCH 2021.