Anticipating audience reaction towards a certain piece of text is integral to several facets of society ranging from politics, research, and commercial industries. Sentiment analysis (SA) is a useful natural language processing (NLP) technique that utilizes both lexical/statistical and deep learning methods to determine whether different sized texts exhibit a positive, negative, or neutral emotion. However, there is currently a lack of tools that can be used to analyse groups of independent texts and extract the primary emotion from the whole set. Therefore, the current paper proposes a novel algorithm referred to as the Multi-Layered Tweet Analyzer (MLTA) that graphically models social media text using multi-layered networks (MLNs) in order to better encode relationships across independent sets of tweets. Graph structures are capable of capturing meaningful relationships in complex ecosystems compared to other representation methods. State of the art Graph Neural Networks (GNNs) are used to extract information from the Tweet-MLN and make predictions based on the extracted graph features. Results show that not only does the MLTA predict from a larger set of possible emotions, delivering a more accurate sentiment compared to the standard positive, negative or neutral, it also allows for accurate group-level predictions of Twitter data.
翻译:敏感分析(SA)是一种有用的自然语言处理(NLP)技术,它使用词汇/统计和深层次的学习方法,确定不同大小的文本是否表现出积极、消极或中性的情绪;然而,目前缺乏可用于分析独立文本组和从整个集中提取主要情感的工具。因此,本文件提出一种新型算法,称为多层Tweet Analyzer(MLTA),它用多层网络(MLNs)对社交媒体文本进行图形化模拟,以便更好地对各套独立的推文进行编码。图表结构能够捕捉复杂生态系统中有意义的关系,而与其他代表方法相比。艺术图示神经网络(GNN)的现状被用来从Tweet-MLN提取信息,并根据提取的图表特征作出预测。结果显示,MLTA不仅从更广大的一组消极的情感角度预测社会媒体文本,还能够提供更准确的Twitter数据。