Mining the Opinionated Web: Classification and Detection of Aspect Contexts for Aspect Based Sentiment Analysis

Araque, Óscar; Zhu, Ganggao; García-Amado, Manuel; Iglesias, Carlos Á.

doi:10.1109/icdmw.2016.0132

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…In a compelling experiment, the authors report similar performance metrics in comparison to more complex neural models in several tasks. Furthermore, [40] presents a model that uses both a measure of semantic similarity and embedding representations. For a comprehensive description of how to exploit word embeddings, we refer the reader to [41].…”

Section: B Word Embeddings and Affect Lexiconsmentioning

confidence: 99%

An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity

Araque

Iglesias

2020

IEEE Access

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The Internet has become an important tool for modern terrorist groups as a means of spreading their propaganda messages and recruitment purposes. Previous studies have shown that the analysis of social signs can help in the analysis, detection, and prediction of radical users. In this work, we focus on the analysis of affect signs in social media and social networks, which has not been yet previously addressed. The article contributions are: (i) a novel dataset to be used in radicalization detection works, (ii) a method for utilizing an emotion lexicon for radicalization detection, and (iii) an application to the radical detection domain of an embedding-based semantic similarity model. Results show that emotion can be a reliable indicator of radicalization, as well as that the proposed feature extraction methods can yield high-performance scores. INDEX TERMS Affective computing, machine learning, natural language processing, terrorism, radicalism.

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Section: B Word Embeddings and Affect Lexiconsmentioning

confidence: 99%

An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity

Araque

Iglesias

2020

IEEE Access

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“…Another interesting work [44] proposes the use of word embeddings within a novel framework designed to minimize computational complexity and demonstrates comparable evaluation metrics to those of more intricate neural models across multiple tasks. Besides, in [10], a merge of semantic similarity and word embeddings approaches is presented.…”

Section: B Word Embeddings and Emotion Lexiconsmentioning

confidence: 99%

Contextualization of a Radical Language Detection System Through Moral Values and Emotions

Real,

Araque

2023

IEEE Access

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“…SVM is a supervised machine learning algorithm can be used for two group classification problems, regression and outlier detection [11]. SVM works on input trained datasets by plotting the points in 2D plane for each category and then draw a hyperplane that best separates the class labels.…”

Section: Support Vector Machinesmentioning

confidence: 99%

A Comprehensive Survey on Aspect Based Word Embedding Models and Sentiment Analysis Classification Approaches

Agrawal

Moparthi

2021

Recent Trends in Intensive Computing

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Sentiment Analysis includes methods and techniques for businesses to understand and analyze customer reviews, feedback and opinion on a particular product or service. Sentiment Analysis uses Natural Language Processing (NLP) tools to analyze feelings or emotions, attitudes, opinions, thoughts, etc. behind the words. Sentiments such as positive, negative and neutral are associated with a particular product. Sentiment analysis is applicable in multi-domains such as customer feedback for a particular product, movie reviews, social and political comments. This survey basically focuses on different aspect-based word embedding models and aspect-based sentiment classification techniques, where the goal is to extract key features from the sentences and classify sentiment on entities at document level. Aspect Based Sentiment Analysis (ABSA) is a technique that concentrates not only the entire sentence but analyses key terms explicitly to predict the polarity as a whole. ABSA model accepts aspect categories and its corresponding aspect terms to generate sentiment corresponding to each aspect from the text corpus. This article provides a comprehensive survey on different word embedding models under CNN framework for aspect extraction and different machine learning techniques applicable for sentiment classification purpose.

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Mining the Opinionated Web: Classification and Detection of Aspect Contexts for Aspect Based Sentiment Analysis

Cited by 9 publications

References 24 publications

An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity

An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity

Contextualization of a Radical Language Detection System Through Moral Values and Emotions

A Comprehensive Survey on Aspect Based Word Embedding Models and Sentiment Analysis Classification Approaches

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