Results 71 to 80 of about 22,491,724 (165)

A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions [PDF]

arXiv
Sentiment Analysis, a popular subtask of Natural Language Processing, employs computational methods to extract sentiment, opinions, and other subjective aspects from linguistic data. Given its crucial role in understanding human sentiment, research in sentiment analysis has witnessed significant growth in the recent years.
arxiv  

A Statistical Parsing Framework for Sentiment Classification [PDF]

arXiv, 2014
We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that employ syntactic parsing results for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence.
arxiv  

Enhanced Sentiment Analysis of Iranian Restaurant Reviews Utilizing Sentiment Intensity Analyzer & Fuzzy Logic [PDF]

arXiv
This research presents an advanced sentiment analysis framework studied on Iranian restaurant reviews, combining fuzzy logic with conventional sentiment analysis techniques to assess both sentiment polarity and intensity. A dataset of 1266 reviews, alongside corresponding star ratings, was compiled and preprocessed for analysis.
arxiv  

Sentiment analysis classification system using hybrid BERT models

Journal of Big Data, 2023
Amira Samy Talaat
semanticscholar   +1 more source

Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue [PDF]

arXiv
Sarcasm Explanation in Dialogue (SED) is a new yet challenging task, which aims to generate a natural language explanation for the given sarcastic dialogue that involves multiple modalities (\ie utterance, video, and audio). Although existing studies have achieved great success based on the generative pretrained language model BART, they overlook ...
arxiv  

When Saliency Meets Sentiment: Understanding How Image Content Invokes Emotion and Sentiment [PDF]

arXiv, 2016
Sentiment analysis is crucial for extracting social signals from social media content. Due to the prevalence of images in social media, image sentiment analysis is receiving increasing attention in recent years. However, most existing systems are black-boxes that do not provide insight on how image content invokes sentiment and emotion in the viewers ...
arxiv  

Emotions are Universal: Learning Sentiment Based Representations of Resource-Poor Languages using Siamese Networks

, 2018
Machine learning approaches in sentiment analysis principally rely on the abundance of resources. To limit this dependence, we propose a novel method called Siamese Network Architecture for Sentiment Analysis (SNASA) to learn representations of resource ...
Bindlish, Ishita, Choudhary, Nurendra, Shrivastava, Manish, Singh, Rajat   +3 more
core  

A survey on sentiment analysis methods, applications, and challenges

Artificial Intelligence Review, 2022
M. Wankhade, A. C. Rao, Chaitanya Kulkarni   +2 more
semanticscholar   +1 more source

SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework with Sentiment-guided Textual Similarity [PDF]

LREC-COLING2024
Recently, sentiment-aware pre-trained language models (PLMs) demonstrate impressive results in downstream sentiment analysis tasks. However, they neglect to evaluate the quality of their constructed sentiment representations; they just focus on improving the fine-tuning performance, which overshadows the representation quality.
arxiv  

A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models [PDF]

arXiv
The COVID-19 pandemic has exacerbated xenophobia, particularly Sinophobia, leading to widespread discrimination against individuals of Chinese descent. Large language models (LLMs) are pre-trained deep learning models used for natural language processing (NLP) tasks. The ability of LLMs to understand and generate human-like text makes them particularly
arxiv