19,693 research outputs found
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
Towards Quantifying the Distance between Opinions
Increasingly, critical decisions in public policy, governance, and business
strategy rely on a deeper understanding of the needs and opinions of
constituent members (e.g. citizens, shareholders). While it has become easier
to collect a large number of opinions on a topic, there is a necessity for
automated tools to help navigate the space of opinions. In such contexts
understanding and quantifying the similarity between opinions is key. We find
that measures based solely on text similarity or on overall sentiment often
fail to effectively capture the distance between opinions. Thus, we propose a
new distance measure for capturing the similarity between opinions that
leverages the nuanced observation -- similar opinions express similar sentiment
polarity on specific relevant entities-of-interest. Specifically, in an
unsupervised setting, our distance measure achieves significantly better
Adjusted Rand Index scores (up to 56x) and Silhouette coefficients (up to 21x)
compared to existing approaches. Similarly, in a supervised setting, our
opinion distance measure achieves considerably better accuracy (up to 20%
increase) compared to extant approaches that rely on text similarity, stance
similarity, and sentiment similarityComment: Accepted in ICWSM '2
Computational Controversy
Climate change, vaccination, abortion, Trump: Many topics are surrounded by
fierce controversies. The nature of such heated debates and their elements have
been studied extensively in the social science literature. More recently,
various computational approaches to controversy analysis have appeared, using
new data sources such as Wikipedia, which help us now better understand these
phenomena. However, compared to what social sciences have discovered about such
debates, the existing computational approaches mostly focus on just a few of
the many important aspects around the concept of controversies. In order to
link the two strands, we provide and evaluate here a controversy model that is
both, rooted in the findings of the social science literature and at the same
time strongly linked to computational methods. We show how this model can lead
to computational controversy analytics that have full coverage over all the
crucial aspects that make up a controversy.Comment: In Proceedings of the 9th International Conference on Social
Informatics (SocInfo) 201
BlogForever D2.6: Data Extraction Methodology
This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform
Aspect Based Sentiment Analysis for Large Documents with Applications to US Presidential Elections 2016
Aspect based sentiment analysis (ABSA) deals with the fine grained analysis of text to extract entities and aspects and analyze sentiments expressed towards them. Previous work in this area has mostly focused on data of short reviews for products, restaurants and services. We explore ABSA for human entities in the context of large documents like news articles. We create the first-of-its-kind corpus containing multiple entities and aspects from US news articles consisting of approximately 1000 annotated sentences in 300 articles. We develop a novel algorithm to mine entity-aspect pairs from large documents and perform sentiment analysis on them. We demonstrate the application of our algorithm to social and political factors by analyzing the campaign for US presidential elections of 2016. We analyze the frequency and intensity of newspaper coverage in a cross-sectional data from various newspapers and find interesting evidence of catering to a partisan audience and consumer preferences by focusing on selective aspects of presidential candidates in different demographics
Online News Headline Extraction
This paper presents the online headline news extraction application. According to
research, today's online news has grown 11 % year over year. Users nowadays are
overwhehned with too much on the internet. The current online news also is not visible
for user to read the news; this is because it is full of the advertisement and other
umelated thing besides the news itself. This paper pmposes the proposal of an EHeadlines
News Extraction Framework that illustrated the extracted information on the
news. This project will only cover the news reported or news available on the local
online English newspaper and at the mean time try to extract the headlines of the news
frrst. At the end of the project, it will highlight the application that can illustrate the
extracted information on the news
William Penn Foundation - Is Philadelphia's Leading Philanthropy Back on Track?
For nearly 70 years, the William Penn Foundation has been a philanthropic giant in the Philadelphia area, leading efforts in the arts, environment and education. While the foundation is largely seen as an effective institution, recent changes in leadership and strategy have challenged the foundation's values of transparency and equity. Encouragingly, William Penn has signaled a renewed commitment to advocacy and organizing that engages affected communities. But there's much work to be done before William Penn is the proactive civic leader its constituents need it to be -- one that breaks through the major problems facing Philadelphia and its underserved communities
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