A Comprehensive Survey on Aspect Based Sentiment Analysis
- URL: http://arxiv.org/abs/2006.04611v1
- Date: Mon, 8 Jun 2020 14:07:58 GMT
- Title: A Comprehensive Survey on Aspect Based Sentiment Analysis
- Authors: Kaustubh Yadav
- Abstract summary: ABSA is known to provide more information about the context than general sentiment analysis.
This survey paper discusses various solutions in-depth and gives a comparison between them.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Aspect Based Sentiment Analysis (ABSA) is the sub-field of Natural Language
Processing that deals with essentially splitting our data into aspects ad
finally extracting the sentiment information. ABSA is known to provide more
information about the context than general sentiment analysis. In this study,
our aim is to explore the various methodologies practiced while performing
ABSA, and providing a comparative study. This survey paper discusses various
solutions in-depth and gives a comparison between them. And is conveniently
divided into sections to get a holistic view on the process.
Related papers
- ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA [50.90538760832107]
This research presents a novel task, Review-Level Opinion Aspect Sentiment Target (ROAST)
ROAST seeks to close the gap between sentence-level and text-level ABSA by identifying every ABSA constituent at the review level.
We extend the available datasets to enable ROAST, addressing the drawbacks noted in previous research.
arXiv Detail & Related papers (2024-05-30T17:29:15Z) - Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment
Analysis [0.6827423171182154]
Aspect-Based Sentiment Analysis (ABSA) is a fine-grained linguistics problem that entails the extraction of multifaceted aspects, opinions, and sentiments from the given text.
We present adaptive masking methods that remove irrelevant tokens based on context to assist in Aspect Term Extraction and Aspect Sentiment Classification subtasks of ABSA.
arXiv Detail & Related papers (2024-02-21T11:33:09Z) - A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends [2.781593421115434]
Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a text.
With the surge of digital opinionated text data, ABSA gained increasing popularity for its ability to mine more detailed and targeted insights.
This paper presents a systematic literature review of ABSA studies with a focus on trends and high-level relationships among these fundamental components.
arXiv Detail & Related papers (2023-11-16T06:01:47Z) - UniSA: Unified Generative Framework for Sentiment Analysis [48.78262926516856]
Sentiment analysis aims to understand people's emotional states and predict emotional categories based on multimodal information.
It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA)
arXiv Detail & Related papers (2023-09-04T03:49:30Z) - Survey of Aspect-based Sentiment Analysis Datasets [55.61047894397937]
Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews.
Numerous yet scattered corpora for ABSA make it difficult for researchers to identify corpora best suited for a specific ABSA subtask quickly.
This study aims to present a database of corpora that can be used to train and assess autonomous ABSA systems.
arXiv Detail & Related papers (2022-04-11T16:23:36Z) - A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and
Challenges [58.97831696674075]
ABSA aims to analyze and understand people's opinions at the aspect level.
We provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements.
We summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage.
arXiv Detail & Related papers (2022-03-02T12:01:46Z) - A Simple Information-Based Approach to Unsupervised Domain-Adaptive
Aspect-Based Sentiment Analysis [58.124424775536326]
We propose a simple but effective technique based on mutual information to extract their term.
Experiment results show that our proposed method outperforms the state-of-the-art methods for cross-domain ABSA by 4.32% Micro-F1.
arXiv Detail & Related papers (2022-01-29T10:18:07Z) - Deep Context- and Relation-Aware Learning for Aspect-based Sentiment
Analysis [3.7175198778996483]
We propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information.
DCRAN significantly outperforms previous state-of-the-art methods by large margins on three widely used benchmarks.
arXiv Detail & Related papers (2021-06-07T17:16:15Z) - Weakly-Supervised Aspect-Based Sentiment Analysis via Joint
Aspect-Sentiment Topic Embedding [71.2260967797055]
We propose a weakly-supervised approach for aspect-based sentiment analysis.
We learn sentiment, aspect> joint topic embeddings in the word embedding space.
We then use neural models to generalize the word-level discriminative information.
arXiv Detail & Related papers (2020-10-13T21:33:24Z) - Aspect-Based Sentiment Analysis in Education Domain [0.0]
We present a comprehensive review of the existing work in ABSA with a focus in the education domain.
ABSA has found itself useful in a wide range of domains.
Being able to understand and find out what students like and don't like most about a course, professor, or teaching methodology can be of great importance for the respective institutions.
arXiv Detail & Related papers (2020-10-03T21:51:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.