J
Jasmina Smailović
Researcher at Jožef Stefan Institute
Publications - 18
Citations - 1434
Jasmina Smailović is an academic researcher from Jožef Stefan Institute. The author has contributed to research in topics: Sentiment analysis & Support vector machine. The author has an hindex of 10, co-authored 18 publications receiving 1130 citations.
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Journal ArticleDOI
Sentiment of Emojis.
TL;DR: The first emoji sentiment lexicon is provided, called the Emoji Sentiment Ranking, and a sentiment map of the 751 most frequently used emojis is drawn, which indicates that most of the emoji are positive, especially the most popular ones.
Journal ArticleDOI
Stream-based active learning for sentiment analysis in the financial domain
TL;DR: Whether the sentiment expressed in Twitter feeds, which discuss selected companies and their products, can indicate their stock price changes is analyzed, and changes in positive sentiment probability can be used as indicators of the changes in stock closing prices.
Journal ArticleDOI
Multilingual Twitter Sentiment Classification: The Role of Human Annotators
TL;DR: It is shown that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large and there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered.
Book ChapterDOI
Predictive Sentiment Analysis of Tweets: A Stock Market Application
TL;DR: Positive sentiment probability is proposed as a new indicator to be used in predictive sentiment analysis in finance and it is shown that sentiment polarity can indicate stock price movements a few days in advance.
Journal ArticleDOI
Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform
Janez Kranjc,Jasmina Smailović,Vid Podpečan,Vid Podpečan,Miha Grčar,Martin Žnidaršič,Nada Lavrač,Nada Lavrač +7 more
TL;DR: ClowdFlows, a cloud-based scientific workflow platform, and its extensions enabling the analysis of data streams and active learning are described, using active learning with a linear Support Vector Machine for learning sentiment classification models to be applied to microblogging data streams.