One of the main benefits of unsupervised learning is that there is no need for labelled data. As a method of this category, latent Dirichlet allocation (LDA) estimates the semantic relations between the words of the text effectively and can play an important role in solving various issues, including emotional analysis in combination with other parameters. In this study, three novel topic models called date sentiment LDA (DSLDA), author–date sentiment LDA (ADSLDA), and pack–author–date sentiment LDA (PADSLDA) are proposed. The proposed models extend LDA through some extra parameters such as date, author, helpfulness, sentiment, and subtopic. The proposed models use helpfulness in the Gibbs sampling algorithm. Helpfulness is a part of readers who found the review helpful. The proposed models divide the words into two categories: the words more affected by the distribution of subtopic and the words more affected by the main topic. In this study, a new concept called pack is introduced, and a new model called PADSLDA is proposed for sentiment analysis at pack level. The proposed models outperformed the baseline models because according to evaluations results, the extra parameters can appropriately affect the generating process of words in a review. Sentiment analysis at the document level, perplexity, and topic coherence are the main parameters used in the evaluations.

中文翻译：

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丰富的潜在狄利克雷分配进行情感分析

无监督学习的主要好处之一是不需要标签数据。作为这种方法，潜在的狄利克雷分配法（LDA）可以有效地估计文本单词之间的语义关系，并且可以在解决各种问题（包括与其他参数结合的情感分析）中发挥重要作用。在这项研究中，提出了三种新颖的主题模型，分别称为日期情感LDA（DSLDA），作者-日期情感LDA（ADSLDA）和打包-作者-日期情感LDA（PADSLDA）。提出的模型通过一些额外的参数扩展了LDA，例如日期，作者，帮助，情感和副主题。提出的模型在Gibbs采样算法中使用了帮助。乐于助人的读者对本评论很有帮助。提出的模型将单词分为两类：单词受子主题分布的影响更大，单词受主要主题的影响更大。在这项研究中，引入了一个新的概念，称为包装，并提出了一种新的模型，称为PADSLDA，用于包装级别的情感分析。所提出的模型优于基准模型，因为根据评估结果，额外的参数可以适当地影响评论中单词的生成过程。在文档级别的情感分析，困惑和主题连贯性是评估中使用的主要参数。所提出的模型优于基准模型，因为根据评估结果，额外的参数可以适当地影响评论中单词的生成过程。在文档级别的情感分析，困惑和主题连贯性是评估中使用的主要参数。所提出的模型优于基准模型，因为根据评估结果，额外的参数可以适当地影响评论中单词的生成过程。在文档级别的情感分析，困惑和主题连贯性是评估中使用的主要参数。