Sentiment analysis/opinion mining is a technique that analyzes people’s opinions, evaluations, sentiments, attitudes, appraisals and emotions to entities like products, organizations, services, issues, individuals, topics, events and their attributes. It is a massive problem space. People tend to express their opinions on anything, such as, a product, service, topic, individual, organization, or an event. Here, the term object represents the entity commented on. Certain private states parts that cannot be judged and observed include the following, beliefs, opinion, emotions and sentiments. The above mentioned aspects are usually expressed in documents using certain subjective words that determine the private states with the help of unique dictionaries like the WordNet or SentiWordNet. The feature selection concept is incorporated in the following tasks such as image classification, data mining, cluster analysis, image retrieval, and pattern recognition. This is observed as a data analysis pre-processing strategy; here a subset from the original data features is thus selected for eliminating the noisy/irrelevant/redundant features. This technique essentially helps in minimizing the incurred computational expenses and helps in enhancing the accuracy level of the data analysis procedures. The Semantic features are meant to concentrate on the relationship between the signifiers such as that of the words, phrases, signs and symbols. A special of semantics called as the linguistic semantics is used for understanding human based expression in opinions and blog. A semantic based feature selection strategy has been introduced for establishing the opinion mining tasks. This introduced semantic based feature selection makes use of the SentiWordNet that is observed to be a lexical resource of the WordNet database extracted terms and is therefore used in the research tasks. Feature set is minimized with the help of the introduced semantic based approaches for the purpose of considering the individual predictive ability words and selection features. Experiments were conducted with the help of the Naïve Bayes, the FLR and the AdaBoost classifiers and the obtained results were compared for understanding and judging the feature selection methods.

情感分析/观点挖掘是一种分析人们对产品，组织，服务，问题，个人，主题，事件及其属性等实体的观点，评估，情感，态度，评估和情感的技术。这是一个巨大的问题空间。人们倾向于对任何事物（例如产品，服务，主题，个人，组织或事件）表达意见。在此，术语“对象”表示被注释的实体。某些无法判断和观察到的私人国家部分包括以下内容，信念，见解，情感和情感。通常在文档中使用某些主观词语来表达上述方面，这些主观词语借助诸如WordNet或SentiWordNet之类的独特词典来确定私有状态。特征选择概念被合并到以下任务中，例如图像分类，数据挖掘，聚类分析，图像检索和模式识别。这被视为数据分析的预处理策略；在此，从原始数据特征中选择一个子集以消除噪声/无关/冗余特征。该技术实质上有助于最大程度地减少计算开销，并有助于提高数据分析过程的准确性。语义特征旨在集中于指示符之间的关系，例如单词，短语，符号和符号的关系。一种特殊的语义称为语言语义，用于理解意见和博客中基于人的表达。已经引入了基于语义的特征选择策略来建立意见挖掘任务。这种引入的基于语义的特征选择利用了SentiWordNet，该SentiWordNet被视为WordNet数据库提取的术语的词汇资源，因此可用于研究任务。借助引入的基于语义的方法，可将特征集最小化，以考虑各个预测能力词和选择特征。在朴素贝叶斯，FLR和AdaBoost分类器的帮助下进行了实验，比较了获得的结果，以了解和判断特征选择方法。这种引入的基于语义的特征选择利用了SentiWordNet，该SentiWordNet被视为WordNet数据库提取的术语的词汇资源，因此可用于研究任务。借助引入的基于语义的方法，可将特征集最小化，以考虑各个预测能力词和选择特征。在朴素贝叶斯，FLR和AdaBoost分类器的帮助下进行了实验，比较了获得的结果，以了解和判断特征选择方法。这种引入的基于语义的特征选择利用了SentiWordNet，该SentiWordNet被视为WordNet数据库提取的术语的词汇资源，因此可用于研究任务。借助引入的基于语义的方法，可将特征集最小化，以考虑各个预测能力词和选择特征。在朴素贝叶斯，FLR和AdaBoost分类器的帮助下进行了实验，并对获得的结果进行了比较，以了解和判断特征选择方法。