11,499 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.
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An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs
The effect of amplifiers, downtoners, and negations has been studied in
general and particularly in the context of sentiment analysis. However, there
is only limited work which aims at transferring the results and methods to
discrete classes of emotions, e. g., joy, anger, fear, sadness, surprise, and
disgust. For instance, it is not straight-forward to interpret which emotion
the phrase "not happy" expresses. With this paper, we aim at obtaining a better
understanding of such modifiers in the context of emotion-bearing words and
their impact on document-level emotion classification, namely, microposts on
Twitter. We select an appropriate scope detection method for modifiers of
emotion words, incorporate it in a document-level emotion classification model
as additional bag of words and show that this approach improves the performance
of emotion classification. In addition, we build a term weighting approach
based on the different modifiers into a lexical model for the analysis of the
semantics of modifiers and their impact on emotion meaning. We show that
amplifiers separate emotions expressed with an emotion- bearing word more
clearly from other secondary connotations. Downtoners have the opposite effect.
In addition, we discuss the meaning of negations of emotion-bearing words. For
instance we show empirically that "not happy" is closer to sadness than to
anger and that fear-expressing words in the scope of downtoners often express
surprise.Comment: Accepted for publication at The 5th IEEE International Conference on
Data Science and Advanced Analytics (DSAA), https://dsaa2018.isi.it
Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
As of February 2016 Facebook allows users to express their experienced
emotions about a post by using five so-called `reactions'. This research paper
proposes and evaluates alternative methods for predicting these reactions to
user posts on public pages of firms/companies (like supermarket chains). For
this purpose, we collected posts (and their reactions) from Facebook pages of
large supermarket chains and constructed a dataset which is available for other
researches. In order to predict the distribution of reactions of a new post,
neural network architectures (convolutional and recurrent neural networks) were
tested using pretrained word embeddings. Results of the neural networks were
improved by introducing a bootstrapping approach for sentiment and emotion
mining on the comments for each post. The final model (a combination of neural
network and a baseline emotion miner) is able to predict the reaction
distribution on Facebook posts with a mean squared error (or misclassification
rate) of 0.135.Comment: 10 pages, 13 figures and accepted at ICAART 2018. (Dataset:
https://github.com/jerryspan/FacebookR
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