1,373 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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TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
This paper describes the participation of the team "TwiSE" in the SemEval
2016 challenge. Specifically, we participated in Task 4, namely "Sentiment
Analysis in Twitter" for which we implemented sentiment classification systems
for subtasks A, B, C and D. Our approach consists of two steps. In the first
step, we generate and validate diverse feature sets for twitter sentiment
evaluation, inspired by the work of participants of previous editions of such
challenges. In the second step, we focus on the optimization of the evaluation
measures of the different subtasks. To this end, we examine different learning
strategies by validating them on the data provided by the task organisers. For
our final submissions we used an ensemble learning approach (stacked
generalization) for Subtask A and single linear models for the rest of the
subtasks. In the official leaderboard we were ranked 9/35, 8/19, 1/11 and 2/14
for subtasks A, B, C and D respectively.\footnote{We make the code available
for research purposes at
\url{https://github.com/balikasg/SemEval2016-Twitter\_Sentiment\_Evaluation}.
NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis
This paper describes two systems that were used by the authors for addressing
Arabic Sentiment Analysis as part of SemEval-2017, task 4. The authors
participated in three Arabic related subtasks which are: Subtask A (Message
Polarity Classification), Sub-task B (Topic-Based Message Polarity
classification) and Subtask D (Tweet quantification) using the team name of
NileTMRG. For subtask A, we made use of our previously developed sentiment
analyzer which we augmented with a scored lexicon. For subtasks B and D, we
used an ensemble of three different classifiers. The first classifier was a
convolutional neural network for which we trained (word2vec) word embeddings.
The second classifier consisted of a MultiLayer Perceptron, while the third
classifier was a Logistic regression model that takes the same input as the
second classifier. Voting between the three classifiers was used to determine
the final outcome. The output from task B, was quantified to produce the
results for task D. In all three Arabic related tasks in which NileTMRG
participated, the team ranked at number one
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Traditional sentiment analysis approaches tackle problems like ternary
(3-category) and fine-grained (5-category) classification by learning the tasks
separately. We argue that such classification tasks are correlated and we
propose a multitask approach based on a recurrent neural network that benefits
by jointly learning them. Our study demonstrates the potential of multitask
models on this type of problems and improves the state-of-the-art results in
the fine-grained sentiment classification problem.Comment: International ACM SIGIR Conference on Research and Development in
Information Retrieval 201
Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election
Social media has become an emerging alternative to opinion polls for public
opinion collection, while it is still posing many challenges as a passive data
source, such as structurelessness, quantifiability, and representativeness.
Social media data with geotags provide new opportunities to unveil the
geographic locations of users expressing their opinions. This paper aims to
answer two questions: 1) whether quantifiable measurement of public opinion can
be obtained from social media and 2) whether it can produce better or
complementary measures compared to opinion polls. This research proposes a
novel approach to measure the relative opinion of Twitter users towards public
issues in order to accommodate more complex opinion structures and take
advantage of the geography pertaining to the public issues. To ensure that this
new measure is technically feasible, a modeling framework is developed
including building a training dataset by adopting a state-of-the-art approach
and devising a new deep learning method called Opinion-Oriented Word Embedding.
With a case study of the tweets selected for the 2016 U.S. presidential
election, we demonstrate the predictive superiority of our relative opinion
approach and we show how it can aid visual analytics and support opinion
predictions. Although the relative opinion measure is proved to be more robust
compared to polling, our study also suggests that the former can advantageously
complement the later in opinion prediction
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