java - Sentiments Analysis Vs emotion Analysis -

java - Sentiments Analysis Vs emotion Analysis -

what difference between sentiments (positive , negative) , emotions in text mining (nlp)? illustration anger negative emotion negative sentiment both seems same. vijay nadadur, creator of sentirank, algorithm ranks sentiment in text, suggest bio sentiments can expressed in binary format (+ve & -ve) in simplistic way. add together further, may varying degrees of +ve , -ve sentiments, , perhaps neutral. however, emotions have multi-dimensions. anger sure -ve sentiment, , sadness, aren't same.

to talk more text mining (nlp based), it's much simpler sentiment analysis hard carry out emotional analysis. next level of sentiment analysis field of intent analysis few researchers have been working on mining out intent chunk of text, seems of high business value.

one view emotion finer-granularity sentiment. mention in question itself, 'anger' , 'sadness' both negative sentiments, clearly, different emotions. emotion analysis can done additional layer on top of (relatively) simple sentiment classification.

=============================================================================== | sentiment polarity | 10 emotion classes | emotion word examples | |-----------------------------------------------------------------------------| | | happiness | happy, joyful, glad | | positive | pleasantness | pleasant, enjoy, nice | | | relief | relief, comfort, solace | |-----------------------------------------------------------------------------| | | fear | scare, fear, frightening | | | sadness | sad, grief, mourn | | | disappointment | bummer, regret, dejected | | negative | unpleasantness | dislike, yuck, irksome | | | loneliness | lonely, withdrawn | | | anxiety | anxiety, worry, distress | | | anger | anger, furious, antagonize | ===============================================================================

note while sentiment classification can binary, ternary or graded scheme (of how +ve or -ve sentiment is), emotion classification much more open interpretation. example, 1 add together love positive emotion , hatred negative emotion. maintain in mind, however, increment number of classes, building accurate classifier becomes more , more difficult. may have start thinking of soft clustering approaches because boundary between 2 emotions may not clear (not ai, it's not clear humans!).

java nlp semantic-analysis emotion