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Opinion-Aware Retrieval Models Based on Sentiment and Intensity of Lexical Features
Mohammad Bahrani, Thomas Roelleke
24 - 31
10.3233/FAIA210228
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 341: Modern Management based on Big Data II and Machine Learning and Intelligent Systems III
Abstract

Sentiment analysis has received much attention in Information Retrieval (IR) and other domains including data mining, machine learning algorithms and NLP. However, when it comes to big data, incorporating sentiment of words into IR models becomes even more important, and as yet no widely accepted standard exists for this task. The contribution of this paper is a framework for quantifying term frequency (TF) variants with sentiments. We propose models derived from the strength of lexical features to improve sentiment-based ranking.

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