Determining sentiment in citation text and analyzing its impact on the proposed ranking index

TL;DR

This paper develops a method to analyze sentiment in citation texts, assigns scores to citations, and introduces the M-index to improve research paper ranking by incorporating sentiment and qualitative factors.

Contribution

It introduces a novel sentiment analysis approach for citation texts and proposes the M-index, a new ranking metric that considers sentiment and other qualitative factors.

Findings

Sentiment can be effectively identified in citation texts using a statistical classifier.

The M-index influences research paper rankings by integrating sentiment scores.

The proposed ranking method offers a more nuanced evaluation of scientific impact.

Abstract

Whenever human beings interact with each other, they exchange or express opinions, emotions, and sentiments. These opinions can be expressed in text, speech or images. Analysis of these sentiments is one of the popular research areas of present day researchers. Sentiment analysis, also known as opinion mining tries to identify or classify these sentiments or opinions into two broad categories - positive and negative. In recent years, the scientific community has taken a lot of interest in analyzing sentiment in textual data available in various social media platforms. Much work has been done on social media conversations, blog posts, newspaper articles and various narrative texts. However, when it comes to identifying emotions from scientific papers, researchers have faced some difficulties due to the implicit and hidden nature of opinion. By default, citation instances are considered inherently positive in emotion. Popular ranking and indexing paradigms often neglect the opinion present while citing. In this paper, we have tried to achieve three objectives. First, we try to identify the major sentiment in the citation text and assign a score to the instance. We have used a statistical classifier for this purpose. Secondly, we have proposed a new index (we shall refer to it hereafter as M-index) which takes into account both the quantitative and qualitative factors while scoring a paper. Thirdly, we developed a ranking of research papers based on the M-index. We also try to explain how the M-index impacts the ranking of scientific papers.