Opinions expressed in social networks can become valuable resources for mining user sentiments. With an automated knowledge discovery technique, online opinions can be categorized into “joy”, “sadness”, “anger”, “surprise”, “fear”, and “disgust”.
They can also be categorized into emotional polarities such as “positive”, “neutral”, or “negative”. The technique has been used in a wide range of fields such as tracking political opinions, classifying consumer’s product reviews, predicting stock market movements, tracking trends in discussion boards, and etc.
Visit the IRA website http://txcdk.unt.edu/iralab/research-tip-week for an online tutorial on how to conduct emotions and polarity classification.
They can also be categorized into emotional polarities such as “positive”, “neutral”, or “negative”. The technique has been used in a wide range of fields such as tracking political opinions, classifying consumer’s product reviews, predicting stock market movements, tracking trends in discussion boards, and etc.
Visit the IRA website http://txcdk.unt.edu/iralab/research-tip-week for an online tutorial on how to conduct emotions and polarity classification.
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