In order to extract the correct sentiment polarity from word of mouth (WOM), a wide-scale and well-organized sentiment lexicon is generally beneficial. SenticNet is one such lexicon. However, it consists of a high proportion of objective words, which are generally considered to be of little use for sentiment classification due to their ambiguity and lack of sentiments. In the literature, there is a dearth of models that focus on this issue. An objective word appearing more frequently in positive sentences than in negative sentences implies a strong relationship in a positive sentiment orientation, and conversely, an objective word appearing more frequently in negative sentences implies a strong relationship in a negative sentiment orientation. Thus, the ratio of an objective word appearing in positive and negative sentences provides the sentiment orientation. Based on this concept, this paper re-assigns the sentiment values to the objective words in SenticNet and builds a revised SenticNet. Three classification techniques, the J48 decision tree, support vector machine, and multilayer perceptron neural network are used for classification. According to the experiments, the proposed models which extract sentiment values from the revised SenticNet, significantly outperform those models which extract sentiment values from the original non-revised SenticNet.

为了从口耳相传（WOM）中提取正确的情感极性，通常可以使用大规模且组织良好的情感词典。SenticNet就是这样的词典之一。但是，它由很大比例的目标词组成，由于它们的含糊不清和缺乏情感，通常被认为对情感分类没有多大用处。在文献中，缺乏针对此问题的模型。与否定句相比，在肯定句中出现频率较高的客观词表示在积极情绪倾向中的紧密关系，相反，在否定句中出现频率较高的客观词表示否定情绪的倾向强烈。从而，出现在正面和负面句子中的客观单词的比率提供了情感倾向。基于此概念，本文将情感值重新分配给SenticNet中的客观词，并构建了修订的SenticNet。使用三种分类技术：J48决策树，支持向量机和多层感知器神经网络进行分类。根据实验，所提出的从修订的SenticNet中提取情感值的模型明显优于那些从原始的未经修订的SenticNet中提取情感值的模型。使用多层感知器神经网络进行分类。根据实验，所提出的从修订的SenticNet中提取情感值的模型明显优于那些从原始的未经修订的SenticNet中提取情感值的模型。使用多层感知器神经网络进行分类。根据实验，所提出的从修订的SenticNet中提取情感值的模型明显优于那些从原始的未经修订的SenticNet中提取情感值的模型。