Determining sentiment in citation text and analyzing its impact on the proposed ranking index
Souvick Ghosh, Dipankar Das, Tanmoy Chakraborty

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
