Results 51 to 60 of about 206,976 (163)

A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas. [PDF]

PeerJ Comput Sci, 2021
Bhamare BR, Prabhu J.
europepmc   +1 more source

Aspect-Based Sentiment Analysis: A Comprehensive Review and Open Research Challenges

IEEE Access
The social web provides a facility for common people to share their views, comments, feedback, and experiences on various social media platforms. Due to these platforms now communication has become easier and it has provided us with opportunities to use ...
Waqas Ahmad, Hikmat Ullah Khan, Fawaz Khaled Alarfaj, Mohammed Alreshoodi   +3 more
doaj   +1 more source

Ensemble BiLSTM: A Novel Approach for Aspect Extraction From Online Text

IEEE Access
Aspect extraction poses a significant challenge in Natural Language Processing (NLP). Extracting explicit and implicit aspects from online text data remains an ongoing challenge despite significant research efforts.
Mikail Muhammad Azman Busst, Kalaiarasi Sonai Muthu Anbananthen, Subarmaniam Kannan, Jayakumar Krishnan, Sridevi Subbiah   +4 more
doaj   +1 more source

A Knowledge-Driven Approach for Automatic Semantic Aspect Term Extraction Using the Semantic Power of Linked Open Data

Applied Sciences
Aspect-Based Sentiment Analysis (ABSA) is a crucial process for assessing customer feedback and gauging satisfaction with products or services. It typically consists of three stages: Aspect Term Extraction (ATE), Aspect Categorization Extraction (ACE ...
Worapoj Suwanpipob, Ngamnij Arch-Int, Warunya Wunnasri   +2 more
doaj   +1 more source

Environmental aspects of resource extraction contracts [PDF]

, 2014
This paper analyzes resource partnerships and their influence on the environmental quality in a resource-rich country by introducing incomplete contracts, imperfect property rights protection, and a lack of valuation for the environment by the government in the South.
openaire   +2 more sources

Leveraging Transformers Ensemble to Improve Aspect Mining in Spanish Reviews

Inteligencia Artificial
Aspect-based sentiment analysis is a process aimed to understanding the sentiment expressed in opinions or reviews about specific features of an entity.
Miguel Ángel Rivero Tapia , Alfredo Simón-Cuevas, Orlando Grabiel Toledano-López   +2 more
doaj  

Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results

, 2016
One of the key tasks of sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. In this work, we focus on using supervised sequence labeling as the base approach to performing the task. Although several extraction methods using sequence labeling methods such as Conditional Random Fields ...
Shu, Lei, Liu, Bing, Xu, Hu, Kim, Annice
openaire   +2 more sources

A Survey on Multimodal Aspect-Based Sentiment Analysis

IEEE Access
Multimodal Aspect-Based Sentiment Analysis (MABSA), as an emerging task in the field of sentiment analysis, has recently received widespread attention. Its aim is to combine relevant multimodal data to determine the sentiment polarity of a given aspect ...
Hua Zhao, Manyu Yang, Xueyang Bai, Han Liu   +3 more
doaj   +1 more source

A Hybrid Frequency Based, Syntax, and Conditional Random Field Method for Implicit and Explicit Aspect Extraction

IEEE Access
Aspect extraction is the most important factor influencing the quality of Aspect-Based Sentiment Analysis (ABSA). Aspect extractions are divided into three approaches: supervised, unsupervised, and hybrid methods.
Mohammad Mashrekul Kabir, Zulaiha Ali Othman, Mohd Ridzwan Yaakub   +2 more
doaj   +1 more source

Domain adaptive English aspect word extraction method based on bidirectional long and short-term memory network and multi-head attention mechanism

Journal of Applied Science and Engineering
English aspect word extraction is a core task of aspect level sentiment analysis. With the continuous development of social networks, more users tend to make decisions based on comment text, and pay more attention to the details of comment text ...
Tianxiao Wang
doaj   +1 more source