Sentiment lexicons (SL) (aka lexical resources) are the repositories of one or several dictionaries that consist of known and precompiled sentiment terms. These lexicons play an important role in performing several different opinion mining tasks. The efficacy of the lexicon-based approaches in performing opinion mining (OM) tasks solely depends on selecting an appropriate opinion lexicon to analyze the text. Therefore, one has to explore the available sentiment lexicons and then select the most suitable resource. Among available resources, SentiWordNet (SWN) is the most widely used lexicon to perform tasks related to opinion mining. In SWN, each synset of WordNet is being assigned the three sentiment numerical scores; positive, negative and objective that are calculated using by a set of classifiers. In this paper, a detailed and comprehensive review of the work related to opinion mining using SentiWordNet is provided in a very distinctive way. This survey will be useful for the researchers contributing to the field of opinion mining. Following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to opinion mining tasks and subtasks (ii) it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels (word, sentences, document, aspect, clause, and concept levels) (iii) this state-of-art review covers each article in the following dimensions: the designated task performed, granularity level of the task completed, results obtained, and feature dimensions, and (iv) lastly it concludes the summary of the related articles according to the granularity levels, publishing years, related tasks (or subtasks), and types of classifiers used. In the end, major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.

情感词典 (SL)（又名词汇资源）是一个或多个词典的存储库，由已知和预编译的情感术语组成。这些词典在执行几种不同的意见挖掘任务中发挥着重要作用。基于词典的方法在执行意见挖掘 (OM) 任务中的有效性完全取决于选择合适的意见词典来分析文本。因此，必须探索可用的情感词典，然后选择最合适的资源。在可用资源中，SentiWordNet (SWN) 是执行与意见挖掘相关任务最广泛使用的词典。在 SWN 中，每个同义词集的 WordNet 被分配了三个情感数值分数；使用一组分类器计算的正、负和目标。在本文中，以非常独特的方式对使用 SentiWordNet 的意见挖掘相关工作进行了详细而全面的回顾。这项调查将对意见挖掘领域的研究人员有所帮助。以下特征使我们的贡献在同类评论中有价值且独一无二：（i）我们的评论根据意见挖掘任务和子任务对现有文献进行了分类（ii）它通过提供以下内容涵盖了意见挖掘领域的一个非常不同的观点：在不同粒度级别（单词、句子、文档、方面、从句、和概念级别）（iii）此最新评论涵盖以下维度的每篇文章：执行的指定任务、完成任务的粒度级别、获得的结果和特征维度，以及（iv）最后总结总结根据粒度级别、出版年份、相关任务（或子任务）和使用的分类器类型对相关文章进行分类。最后，还讨论了与基于词典的意见挖掘方法相关的主要挑战和任务。