1. IDENTIFICATION OF CERTAIN EMOTIONS IN TEXT IDENTIFICATION OF CERTAIN EMOTIONS IN TEXT (NATURAL LANGUAGE PROCESSING) Mentor: Prof. Amitabha Mukherjee Pranjal Singh10511spranjal@iitk.ac.in Divyanshu Bhartiya10250divbhar@iitk.ac.in CS365: ARTIFICIAL INTELLIGENCE 5/11/2015 1

2. IMPORTANCE + SCOPE 5/11/2015 2

3. METHODOLOGY: OVERVIEW SUPERVISED LEARNING Parsing of text using “Stanford PCFG Parser” Sentiment Categorization using Supervised Learning(Alchemy API) UNSUPERVISED LEARNING Removal of Stop-Words Created term by document matrix Applied PLSA on this matrix 5/11/2015 3

4. SUPERVISED LEARNING PCFG parser 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” negative -0.357542 </score Sentiment Analysis Result: 5/11/2015 4

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” EmotionProbability Sad1.00000000e+000 Fear1.24334077e-086 Joy0.00000000e+000 Guilt0.00000000e+000 Shame0.00000000e+000 Anger7.25160223e-034 Disgust1.27906894e-132 5/11/2015 5

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

7. SAMPLE RESULT: 5/11/2015 7

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. 5/11/2015 8