Emotion Recognition from Poems by Maximum Posterior Probability Sreeja. P.S Department of Computer Science CEG, Anna University Chennai, India srj_ps@yahoo.com G.S. Mahalakshmi Department of Computer Science CEG, Anna University Chennai, India gsmaha@annauniv.edu Abstract—Poem is a type of literature designed to convey, ideas, emotions, and experiences in a brilliant way. In this article, we discuss the automatic emotion recognition of poems written in English. This is a pioneering approach in emotion recognition from poems. Emotions from the poems, classified into nine emotions, based on ‘Navarasa’ under ‘Rasa Theory’ which is described in ‘Natyashastra’ written by ‘Bharatha Muni.' The nine basic emotions such as Love, Sadness, Anger, Hatred, Fear, Surprise, Courage, Joy, and Peace, classified as “Navarasa”. As to our knowledge, we are not familiar about a text corpus of poems based on nine emotions, We have manually created an emotion tagged corpus from poems in English. The corpus created is from an exhaustive collection of poems of Indian poets from the period 1850-1950. The poems are mined from the web, and we applied ten cross fold Naïve Bayes classifier to recognize the emotion of a poem by maximum likelihood probability Keywords-Emotion Recognition; Emotion Analysis; Emotion Annotation; Emotion Corpus; Naïve Bayes Classifier I. INTRODUCTION Emotion recognition is one of the dynamic fields in Natural language processing. Recent research into human-machine interaction has a vital role in emotional reactions. Automatic emotion recognition phenomena are studied in opinion mining, market analysis, affective computing, Sentiment Analysis, etc. Humans by nature are emotionally affected by reading poems. Emotion recognition in poems is an important task, as poetry databases or poem websites are growing and number. The retrieval of poem or lyric by emotion has various applications such as verse selection from poetry websites. It can be applied in machine translation of poems, poetic therapy, etc. There are many datasets available for emotion recognition but limited to our knowledge we haven’t find emotion corpus for poems in Nine category such as Love, Sadness, Joy, Fear, Hatred, Courage, Anger, Surprise and Peace. In this paper, we focus on an approach to recognize emotion from English poems based on “Navarasa” described in “Natyashastra.” We introduce a novel corpus of English poems of Indian Poets in 1850-1950. In this article, emotions are categorized into nine types such as Love, Sadness, Joy, Fear, Hatred, Courage, Anger, Surprise and Peace. ‘Navarasa’ is based on ‘Rasa Theory’ given by ‘Bharatha Muni’ in ‘Natyashastra. ‘Bharata Muni,' is the father of Indian poetics and defines that “rasa” is a thing of relishing or enjoyment of something. Thus Rasa means in poetical viewpoint is a poetical pleasure. Other ways we can say that it is an aesthetic experience of a piece of art. NatyaShastra is an Indian text dated between 2nd century BC and 2nd century AD that analyzes all features of performing art. It is also called the fifth Veda because of its importance [12] Theory of ‘Rasa’ is defined in chapters VI and VII of ‘NatyaShastra.' Bharat Muni explains ‘Rasa’ as “Vibhaavaanubhaav vyabhichaari samyogat ras nishpatti,” The meaning of this verse is, out of the blend (samayoga), of the causes (vibhava), the consequents (anubhava) and the passing mental status (vyabhichari), brings the birth of emotions (rasa). II. RELATED WORK Over the past semi-century, there have been multiple approaches to emotion recognition from the text. There are many emotion recognition types of research based on probabilistic approach. Reference [2], used a simple Natural Language Parser for keyword spotting, phrase length measurement, and emotion identification.[ CHUNG-HSIEN WU et.al] used semantic labels and Separable mixture model to identify emotions.They manually generated the rules for emotion, semantic labels and attitudes. With the help of emotion generation rules, semantic labels and attitudes emotion association rules are automatically derived using priori algorithm. Reference [1] constructed of a large dataset of News headlines are annotated for [7] six basic emotions such as Anger, Disgust, Fear, Joy, Sadness, and Surprise. They proposed LSA and Naïve Bayes Classifier and evaluated several knowledge-based methods for the automatic identification of these emotions in text. Reference [9] constructed a Japanese Emotion Corpus and through the analysis of corpus they have identified the emotions automatically. The advantage is that it can yield high precision. But the main disadvantage is impossible to determine the Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500 International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016 36