Statistical EmoText
Statistical EmoText (Statistical Emotional Text Analyzer) illustrates a language- and domain-independent approach to opinion mining. For reliable result classified texts should contain not less than 200 words. The system builds a frequency vector for text words and uses the SMO classifier from the WEKA data mining toolkit for text classification.
The approach was tested on the following English corpora: Pang corpus, Berardinelli movie review corpus, a corpus with spontaneous dialogues (the SAL corpus), and a corpus with product reviews. For more information about the statistical affect sensing see here.
In the next form enter the text to classify, e.g. a movie review and press the Classify button. You can take movie reviews from here.
Semantic EmoText
Semantic EmoText (Semantic Emotional Text Analyzer) demonstrates an approach to semantic textual affect sensing. In contrast to the statistical approach above, the semantic affect analyzer facilitates affect sensing in short texts using semantics of short emotional utterances, in particular, commonsense of their words and uses hereby the SPIN Semantic Parser for Dialogue Spoken Systems and the Stanford Statistical Lexical Parser. For more information about the semantic affect sensing see here.
In the next form enter an emotional utterance to classify!