Browsing by Author "Bravo-Marquez, Felipe"
Now showing items 1-5 of 13
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Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis
(University of Waikato, 2017)The most popular sentiment analysis task in Twitter is the automatic classification of tweets into sentiment categories such as positive, negative, and neutral. State-of-the-art solutions to this problem are based on ... -
Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis
(IOS Press, 2016-01-01)The classification of tweets into polarity classes is a popular task in sentiment analysis. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. ... -
Building a Twitter opinion lexicon from automatically-annotated tweets
(Elsevier, 2016-09-15)Opinion lexicons, which are lists of terms labelled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations ... -
Combining strengths, emotions and polarities for boosting Twitter sentiment analysis
(2013-01-01)Twitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as ... -
Determining word–emotion associations from tweets by multi-label classification
(IEEE Computer Society, 2016)The automatic detection of emotions in Twitter posts is a challenging task due to the informal nature of the language used in this platform. In this paper, we propose a methodology for expanding the NRC word-emotion ...
