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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 601–625 of 630 papers

TitleStatusHype
Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies—0
A Linked Data Model for Multimodal Sentiment and Emotion Analysis—0
A Meta-Learning Approach for Few-Shot (Dis)Agreement Identification in Online Discussions—0
AMI\&ERIC: How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment Analysis—0
A Multi-Granularity Opinion Summarization Method—0
A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining—0
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews—0
A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language—0
A Multi-View Sentiment Corpus—0
Analysing Market Sentiments: Utilising Deep Learning to Exploit Relationships within the Economy—0
Analysing Public Transport User Sentiment on Low Resource Multilingual Data—0
Analysis of Inferences in Chinese for Opinion Mining—0
Analysis of opinionated text for opinion mining—0
An annotated corpus of quoted opinions in news articles—0
An combined sentiment classification system for SIGHAN-8—0
An Empirical Examination of Online Restaurant Reviews—0
A Neural Network Component for Knowledge-Based Semantic Representations of Text—0
An Evaluation of the Brazilian Portuguese LIWC Dictionary for Sentiment Analysis—0
An Integrated NPL Approach to Sentiment Analysis in Satisfaction Surveys—0
Annotating Attribution Relations in Arabic—0
Annotating Italian Social Media Texts in Universal Dependencies—0
Annotating Opinions in German Political News—0
Annotating the Focus of Negation in Japanese Text—0
Annotating the Interaction between Focus and Modality: the case of exclusive particles—0
An opinion about opinions about opinions: subjectivity and the aggregate reader—0
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