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Emotion Identification in Text using Artificial Intelligence

Explore sentiment analysis methods to identify emotions in text using supervised and unsupervised learning techniques. Compare PLSA with LSA and enhance emotion detection by updating term-document matrices.

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Emotion Identification in Text using Artificial Intelligence

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  1. CS365: ARTIFICIAL INTELLIGENCE IDENTIFICATION OF CERTAIN EMOTIONS IN TEXT(NATURAL LANGUAGE PROCESSING) Mentor: Prof. AmitabhaMukherjee Pranjal Singh 10511 spranjal@iitk.ac.in DivyanshuBhartiya 10250 divbhar@iitk.ac.in

  2. Importance + scope

  3. METHODOLOGY: OVERVIEW

  4. Supervised learning Sentiment Analysis Result: “He might have lung cancer . It s just a rumor ... but it makes sense . He is very depressed and that s just the beginning of things” PCFG parser result : <type> negative </type> <score> -0.357542 </score

  5. UNSupervised learning “He might have lung cancer . It s just a rumor ... but it makes sense . He is very depressed and that s just the beginning of things”

  6. PLSA • PLSA aims to discover something about the meaning behind the words; about the topics in the documents

  7. Sample RESULT:

  8. Difference in our approach • Use of PLSA instead of LSA which “Carlos Strapparava” and “Rada Mihalcea” have done in their work. • Instead of annotated corpus which is used by “Carlos Strapparava” and “Rada Mihalcea” for applying LSA we are updating the term document matrix for later use of PLSA. • Removal of certain words that don’t contribute to emotions.

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