2,647 research outputs found
Semantic Sentiment Analysis of Twitter Data
Internet and the proliferation of smart mobile devices have changed the way
information is created, shared, and spreads, e.g., microblogs such as Twitter,
weblogs such as LiveJournal, social networks such as Facebook, and instant
messengers such as Skype and WhatsApp are now commonly used to share thoughts
and opinions about anything in the surrounding world. This has resulted in the
proliferation of social media content, thus creating new opportunities to study
public opinion at a scale that was never possible before. Naturally, this
abundance of data has quickly attracted business and research interest from
various fields including marketing, political science, and social studies,
among many others, which are interested in questions like these: Do people like
the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about
the Brexit? Answering these questions requires studying the sentiment of
opinions people express in social media, which has given rise to the fast
growth of the field of sentiment analysis in social media, with Twitter being
especially popular for research due to its scale, representativeness, variety
of topics discussed, as well as ease of public access to its messages. Here we
present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the
Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition.
201
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
A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts
Wide usage of social media platforms has increased the risk of aggression,
which results in mental stress and affects the lives of people negatively like
psychological agony, fighting behavior, and disrespect to others. Majority of
such conversations contains code-mixed languages[28]. Additionally, the way
used to express thought or communication style also changes from one social
media plat-form to another platform (e.g., communication styles are different
in twitter and Facebook). These all have increased the complexity of the
problem. To solve these problems, we have introduced a unified and robust
multi-modal deep learning architecture which works for English code-mixed
dataset and uni-lingual English dataset both.The devised system, uses
psycho-linguistic features and very ba-sic linguistic features. Our multi-modal
deep learning architecture contains, Deep Pyramid CNN, Pooled BiLSTM, and
Disconnected RNN(with Glove and FastText embedding, both). Finally, the system
takes the decision based on model averaging. We evaluated our system on English
Code-Mixed TRAC 2018 dataset and uni-lingual English dataset obtained from
Kaggle. Experimental results show that our proposed system outperforms all the
previous approaches on English code-mixed dataset and uni-lingual English
dataset.Comment: 10 pages, 5 Figures, 6 Tables, accepted at CoDS-COMAD 202
Role of sentiment classification in sentiment analysis: a survey
Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results
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