Ontology-Based Entity Coreference Resolution For Sentiment Analysis

Tien, Le Thi Thuy; Thanh, Tho Quan; Thi, Tuoi Phan

doi:10.1145/3155133.3155168

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…ASR is the module that recognises sentiments, aspects, and semantic labels based on a sentiment ontology. We adopt the Sentiment Ontology built from the work [25] to identify the words in Corpus12 and to determine which classes these words belong to. In this In our system, the Sentiment Ontology also describes other relationships between the instances of classes, such as isAspect, hasAspect (Object-Aspect co-reference), isSentiment, hasSentiment (Aspect-Sentiment co-reference), isPositive, hasPositive (positive Aspect-Sentiment relationship between), isNegative, hasNegative (negative Aspect-Sentiment relationship), and isSubClass (conceptual classes relationship).…”

Section: B Aspect and Sentiment Recognizermentioning

confidence: 99%

Deep Learning Using Context Vectors to Identify Implicit Aspects

2023

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Aspects extraction is the key task in the sentiment analysis problem, which includes extraction of both explicit and implicit aspects. Identifying implicit aspects is not a new task in sentiment analysis, but it still presents many challenges. Numerous studies have addressed the issue and offered different approaches, but there are still fundamental challenges as follows: solving the context word problem; the domain-specific problem; and the implicit aspects represented by sentiment words that do not appear explicitly in the text. This paper proposes a method for identifying implicit aspects of sentiment words. To address these above challenges, the system is built on the foundation of deep learning with context vectors, dependency grammar, anaphora coreference resolution, and sentiment ontology. The dependency parser and the entity coreference resolver are adopted to filter out the sentiment-aspect pairs data generated from embedding context words. Then, a fine-tuned technique is applied, and the classifier corresponding to each sentiment word is built to identify implicit aspects. This combination has produced positive results, reaching almost 90% accuracy.

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Section: B Aspect and Sentiment Recognizermentioning

confidence: 99%

Deep Learning Using Context Vectors to Identify Implicit Aspects

2023

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Machine learning using context vectors for object coreference resolution

2021

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Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

et al. 2019

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The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across different platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper offers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses different neural network-based approaches for analyzing sentiment. Finally, these different approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.

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Ontology-Based Entity Coreference Resolution For Sentiment Analysis

Cited by 3 publications

References 17 publications

Deep Learning Using Context Vectors to Identify Implicit Aspects

Deep Learning Using Context Vectors to Identify Implicit Aspects

Machine learning using context vectors for object coreference resolution

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

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