Lyrics play a significant role in conveying the song's mood and are information to understand and interpret music communication. Conventional natural language processing approaches use translation of the Hindi text into English for analysis. This approach is not suitable for lyrics as it is likely to lose the inherent intended contextual meaning. Thus, the need was identified to develop a system for Devanagari text analysis. The data set of 300 song lyrics with equal distribution in five different moods is used for the experimentation. The proposed system performs contextual mood analysis of Hindi song lyrics in Devanagari text format. The contextual analysis is stored as a knowledge base, updated using an incremental learning approach with new data. Contextual knowledge graph with moods and associated important contextual terms provides the graphical representation of the lyric data set used. The testing results show 64% accuracy for the mood prediction. This work can be easily extended to applications related to Hindi literary work such as summarization, indexing, contextual retrieval, context-based classification and grouping of documents.
翻译:传统自然语言处理方法使用印地语文字翻译成英文进行分析。这一方法不适合歌词,因为它有可能失去固有的背景含义。因此,确定需要开发Devanagari文字分析系统。实验中使用了在五个不同情绪中平等分布的300个歌曲歌词数据集。拟议系统以Devanagari文字格式对印地语歌词进行背景情绪分析。背景分析存储为知识库,使用一种渐进学习方法与新数据进行更新。带有情绪和相关重要背景术语的背景知识图提供了所使用词类数据集的图形表述。测试结果显示情绪预测64%的准确性。这项工作很容易扩大到与印地语文学工作有关的应用,如概括、索引、背景检索、背景分类和文件分组。