121,622 research outputs found
Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review
Sentiment analysis (SA) is the automated process of detecting and
understanding the emotions conveyed through written text. Over the past decade,
SA has gained significant popularity in the field of Natural Language
Processing (NLP). With the widespread use of social media and online platforms,
SA has become crucial for companies to gather customer feedback and shape their
marketing strategies. Additionally, researchers rely on SA to analyze public
sentiment on various topics. In this particular research study, a comprehensive
survey was conducted to explore the latest trends and techniques in SA. The
survey encompassed a wide range of methods, including lexicon-based,
graph-based, network-based, machine learning, deep learning, ensemble-based,
rule-based, and hybrid techniques. The paper also addresses the challenges and
opportunities in SA, such as dealing with sarcasm and irony, analyzing
multi-lingual data, and addressing ethical concerns. To provide a practical
case study, Twitter was chosen as one of the largest online social media
platforms. Furthermore, the researchers shed light on the diverse application
areas of SA, including social media, healthcare, marketing, finance, and
politics. The paper also presents a comparative and comprehensive analysis of
existing trends and techniques, datasets, and evaluation metrics. The ultimate
goal is to offer researchers and practitioners a systematic review of SA
techniques, identify existing gaps, and suggest possible improvements. This
study aims to enhance the efficiency and accuracy of SA processes, leading to
smoother and error-free outcomes
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
Comprehensive Review of Opinion Summarization
The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe
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