Decision making by analyzing the underlying sentiment has become one of the challenging task with the explosion of rich source of user generated, diverse contents on the web. Sentiment analysis can ease the process of obtaining an overall sentiment by processing millions of reviews or documents altogether. A review or any textual data consists of numerous keywords, which hold different weightage based on various factors. An efficient ranking technique for those keywords is proposed in this paper in order to aid in the sentiment analysis process. A co-occurrence graph based statistical approach is adopted in this paper to find the global rank of the keywords. A novel node weighting technique is proposed, which will be used for the improvisation of the state of art method: Node and Edge Rank (NE-Rank) along with degree, to rank the keywords. The algorithm considers five different influential parameters to propose the node weighting technique. Also, a keyword may hold bi-polarity and depict different polarity according to the domain of application. Therefore, this paper proposes a novel, well organized and efficient sentiment analysis model using a graph based keyword ranking and domain specific rank based polarity assignment algorithm. The role or impact of an important keyword will be always more in comparison to a weaker one for determining the polarity of the review. Thus, the rank based polarity assignment technique is proposed with the use of the global ranks of keywords, to solve the domain dependency problem of the keywords. The proposed model is evaluated and validated using four different existing models for four different customer review datasets.

随着网络上用户生成的各种内容的丰富来源的激增，通过分析基本情绪进行决策已成为一项具有挑战性的任务。情感分析可以通过共处理数百万条评论或文档来简化获得总体情感的过程。评论或任何文本数据由众多关键字组成，这些关键字根据各种因素而具有不同的权重。本文提出了一种针对这些关键词的有效排序技术，以辅助情感分析过程。本文采用基于共现图的统计方法来寻找关键词的整体排名。提出了一种新颖的节点加权技术，该技术将用于改进现有技术方法：节点和边缘等级（NE-Rank）以及程度，以对关键字进行排名。该算法考虑了五个不同的影响参数来提出节点加权技术。同样，关键字可以保持双极性，并根据应用领域描述不同的极性。因此，本文提出了一种新颖，组织良好且高效的情感分析模型，该模型使用基于图的关键字排名和基于特定领域排名的极性分配算法。与确定评论极性的弱关键字相比，重要关键字的作用或影响总是更大。因此，提出了基于关键字的全局等级的基于等级的极性分配技术，以解决关键字的域依赖性问题。针对四个不同的客户评论数据集，使用四个不同的现有模型对提议的模型进行了评估和验证。