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Graph based sentiment analysis using keyword rank based polarity assignment

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Abstract

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.

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  1. N.I.T. Silchar, Assam, India

    Monali Bordoloi & Saroj Kr. Biswas

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  1. Monali Bordoloi
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Correspondence to Monali Bordoloi.

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Bordoloi, M., Biswas, S.K. Graph based sentiment analysis using keyword rank based polarity assignment. Multimed Tools Appl 79, 36033–36062 (2020). https://doi.org/10.1007/s11042-020-09289-4

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