5,754 research outputs found
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
empathi: An ontology for Emergency Managing and Planning about Hazard Crisis
In the domain of emergency management during hazard crises, having sufficient
situational awareness information is critical. It requires capturing and
integrating information from sources such as satellite images, local sensors
and social media content generated by local people. A bold obstacle to
capturing, representing and integrating such heterogeneous and diverse
information is lack of a proper ontology which properly conceptualizes this
domain, aggregates and unifies datasets. Thus, in this paper, we introduce
empathi ontology which conceptualizes the core concepts concerning with the
domain of emergency managing and planning of hazard crises. Although empathi
has a coarse-grained view, it considers the necessary concepts and relations
being essential in this domain. This ontology is available at
https://w3id.org/empathi/
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Review and Analysis of Product Review Sentiment Analysis using Improved Machine Learning Techniques
Sentiment analysis has emerged as a crucial task in the era of big data and social media. Understanding the sentiments expressed in product reviews is vital for businesses to gauge customer satisfaction and make informed decisions. This research paper presents a design simulation and assessment of product review sentiment analysis using improved machine learning techniques. The aim is to develop a robust sentiment analysis model that outperforms existing approaches in accuracy and efficiency. We propose a novel methodology that combines advanced feature extraction, sentiment classification algorithms, and model optimization techniques.The introduction provides an overview of the importance of sentiment analysis in the context of product reviews and the challenges faced by conventional methods. It also outlines the objectives and scope of this research. The related works section presents a comprehensive review of existing literature and highlights the limitations of current approaches. The proposed methodology section describes the technical details of our enhanced machine learning approach and the reasoning behind the selected techniques.In the analysis of sample results, we evaluate the performance of our proposed model on a diverse dataset of product reviews. We present the accuracy, precision, recall, and F1-score metrics, along with a comparison to baseline models and state-of-the-art sentiment analysis systems. Furthermore, we discuss the model's robustness in handling various types of products and reviews.
Our research demonstrates significant improvements in sentiment analysis accuracy compared to traditional methods. We introduce tables and graphs to illustrate the model's performance in different scenarios and identify its strengths and weaknesses. The paper concludes by discussing the implications of our findings, potential applications in industry, and directions for future research. Overall, this research contributes to the advancement of sentiment analysis techniques and provides a valuable resource for businesses aiming to enhance their understanding of customer sentiments through product reviews
Comprehending Semantic Types in JSON Data with Graph Neural Networks
Semantic types are a more powerful and detailed way of describing data than
atomic types such as strings or integers. They establish connections between
columns and concepts from the real world, providing more nuanced and
fine-grained information that can be useful for tasks such as automated data
cleaning, schema matching, and data discovery. Existing deep learning models
trained on large text corpora have been successful at performing single-column
semantic type prediction for relational data. However, in this work, we propose
an extension of the semantic type prediction problem to JSON data, labeling the
types based on JSON Paths. Similar to columns in relational data, JSON Path is
a query language that enables the navigation of complex JSON data structures by
specifying the location and content of the elements. We use a graph neural
network to comprehend the structural information within collections of JSON
documents. Our model outperforms a state-of-the-art existing model in several
cases. These results demonstrate the ability of our model to understand complex
JSON data and its potential usage for JSON-related data processing tasks
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