Handling Negation for Sentiment Analysis: A Case Study Using Dependency Parse Tree on Amazon Reviews of Kindle

Published in Journal

Anusandhan (RNTUJ-AN) [RNTUJ-AN] (Vol:17/ Issue: 26) DOI: [NA]
Authors: Smita Suresh Daniel Ani Thomas Neelam Sahu
Subjects: Technology & Management

Year: April, 2019
Volume: 17 / Issue: 26
Pages: 1905-1910
Publisher: RNTU
Source:
ISSN: 2278-4187
E-ISSN: 2457-0656
DOI: NA
Published URL:
Published On: April, 2019

Article Details

Handling Negation for Sentiment Analysis: A Case Study Using Dependency Parse Tree on Amazon Reviews of Kindle | Original Article

Smita Suresh Daniel Ani Thomas Neelam Sahu in Anusandhan (RNTUJ-AN) | Multidisciplinary Academic Research

ABSTRACT:

The process of sentiment analysis is a task of detecting, extracting and classifying sentiments expressed in texts. It includes the understanding of the meaning of words within the text through natural language processing rules using dependency based parse trees, using grammatical relations among words to model a sentence, and hence to determine words that are affected by negation. This paper presents a framework for identifying. Calculating and representing the presence of negation in textual data using dependency parsers. It includes a list of rules for negative polarity identification and calculation. These negation rules are designed to improve sentiment analysis. This paper is a demonstration of an approach for identifying the scope of negation in a review and its calculation for the Amazon product- Kindle dataset.