Sentiment Analysis of Document Based on Annotation

TL;DR

This paper introduces a tool that assesses the quality and usefulness of documents by analyzing annotations such as comments, highlights, and underlines, including meta-annotations, to infer collective sentiment.

Contribution

The novel contribution is a method that considers various types of annotations and their hierarchical relationships to determine the overall sentiment and quality of a document.

Findings

The tool effectively classifies collective sentiment as positive, negative, or objective.

It computes sentiment scores based on annotations including comments and highlights.

Demonstrated application on research papers shows practical utility.

Abstract

I present a tool which tells the quality of document or its usefulness based on annotations. Annotation may include comments, notes, observation, highlights, underline, explanation, question or help etc. comments are used for evaluative purpose while others are used for summarization or for expansion also. Further these comments may be on another annotation. Such annotations are referred as meta-annotation. All annotation may not get equal weightage. My tool considered highlights, underline as well as comments to infer the collective sentiment of annotators. Collective sentiments of annotators are classified as positive, negative, objectivity. My tool computes collective sentiment of annotations in two manners. It counts all the annotation present on the documents as well as it also computes sentiment scores of all annotation which includes comments to obtain the collective sentiments about the document or to judge the quality of document. I demonstrate the use of tool on research paper.