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Opinion Mining

Identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral (Source: Oxford Languages)

Image Source: Deep learning for sentiment analysis: A survey

Papers

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Showing 526–550 of 630 papers

TitleStatusHype
Opinion Mining in Newspaper Articles by Entropy-Based Word Connections—0
Implicit Feature Detection via a Constrained Topic Model and SVM—0
Exploiting Domain Knowledge in Aspect Extraction—0
Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic—0
Is Twitter A Better Corpus for Measuring Sentiment Similarity?—0
Automatically Detecting and Attributing Indirect Quotations—0
Sentiment Analysis in the News—0
Using Self-Organizing Maps for Sentiment Analysis—0
Performance Investigation of Feature Selection Methods—0
General Purpose Textual Sentiment Analysis and Emotion Detection Tools—0
Detecting Negated and Uncertain Information in Biomedical and Review Texts—0
Mining Fine-grained Opinion Expressions with Shallow Parsing—0
Improving Web 2.0 Opinion Mining Systems Using Text Normalisation Techniques—0
Opinion Learning from Medical Forums—0
Sentiment Analysis of Reviews: Should we analyze writer intentions or reader perceptions?—0
TwitIE: An Open-Source Information Extraction Pipeline for Microblog TextCode0
Automatic extraction of contextual valence shifters.—0
Annotating the Interaction between Focus and Modality: the case of exclusive particles—0
Comparing Multilingual Comparable Articles Based On Opinions—0
Aspect-Oriented Opinion Mining from User Reviews in Croatian—0
Frame Semantics for Stance Classification—0
Identifying Sentiment Words Using an Optimization-based Model without Seed Words—0
Bi-directional Inter-dependencies of Subjective Expressions and Targets and their Value for a Joint Model—0
An annotated corpus of quoted opinions in news articles—0
Identifying Opinion Subgroups in Arabic Online Discussions—0
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