A study by which the opinions, emotions, evaluations, appraisals, and sentiments of people towards different entities is expressed in the form of a text is called Sentiment Analysis (SA). The primary task of a sentiment analysis which is based on the aspects is the extraction of various aspects of the entities and the determination of the sentiments that correspond to the terms of aspects which are commented in the review document. Recently, there is a huge rise in interest to make an identification of various sentiments and aspects at the same time. Feature selection in terms of aspects of entity plays a crucial role in deciding the efficiency of the sentiment analysis; hence the Minimum Spanning Tree (MST) is used for feature selection.The MST has certain major advantages such as being computable quickly. The selection of optimal features to aid in better accuracy of classification is done through MST optimized with Cuckoo search algorithm. The features in sentiment analysis are classified using Random Forest (RF) and Ada Boost classifiers.The Random Forest (RF) is probably the most accurate among all algorithms of learning available today. The Ada Boost algorithm has a performance that is extremely good owing to its ability to be able to generate the expanding diversity. This was done in order to bring about an improvement in the final ensemble, as it contained several weak classifiers.

以文本形式表达人们对不同实体的意见，情感，评估，评价和情感的研究称为情感分析（SA）。基于方面的情感分析的主要任务是提取实体的各个方面，并确定与评论文档中注释的方面相关的情感。近来，人们对同时识别各种情绪和方面的兴趣大增。就实体方面而言，特征选择在决定情感分析的效率方面起着至关重要的作用。因此，最小生成树（MST）用于特征选择。MST具有某些主要优点，例如可快速计算。通过使用Cuckoo搜索算法进行了优化的MST，可以选择有助于提高分类准确度的最佳特征。使用随机森林（RF）和Ada Boost分类器对情感分析的功能进行分类。在当今可用的所有学习算法中，随机森林（RF）可能是最准确的。由于Ada Boost算法能够产生扩展的多样性，因此其性能非常好。这样做是为了改进最终合奏，因为它包含几个弱分类器。在当今可用的所有学习算法中，随机森林（RF）可能是最准确的。由于Ada Boost算法能够产生扩展的多样性，因此其性能非常好。这样做是为了改进最终合奏，因为它包含几个弱分类器。在当今可用的所有学习算法中，随机森林（RF）可能是最准确的。由于Ada Boost算法能够产生扩展的多样性，因此其性能非常好。这样做是为了改进最终合奏，因为它包含几个弱分类器。