Emotion Recognition from Poems by Maximum ...

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Emotion Recognition from Poems by Maximum Posterior Probability Sreeja. P.S

G.S. Mahalakshmi

Department of Computer Science CEG, Anna University Chennai, India [email protected]

Department of Computer Science CEG, Anna University Chennai, India [email protected]

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

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

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emotion of words which is not in the corpus. Reference[6] has used conditional random field classifier to recognize emotion in a sentence of Bengali blogs. Reference [7] used SVM with tenfold cross-validation to identify emotion in Hierarchical Structure. This method recognizes neutrality, polarity, and emotion of a text hierarchically. According to [8], expert knowledge is mandatory to distinguish emotional keywords for the construction of emotion categories. He used latent semantic analysis to identify emotions. Reference [10] identified emotions in poems using VSM (vector space model) with cosine similarity. The English poems are collected from the web of different poets from worldwide. They have modified traditional VSM with some changes and compared the results. They found that traditional VSM is better than the Modified versions. Reference [11] used a probabilistic approach to identify emotions from the poem. They compared the probabilistic approach with VSM (Vector space model). Again proved that traditional vector space model with cosine similarity give better accuracy than probabilistic approach and other modified VSM methods. Many of the related work mentioned in reference did the labeling of emotions under six [7] or five categories such as (love, sadness, fear, disgust, anger, and surprise). Emotion categorization is different for different domains. Emotions in education field can mainly classify into boredom and interesting. As we have taken poems as our field, we thought to categorize into nine emotions such as (Love, Sadness, Hatred, Anger, Joy, Fear, Courage, Surprise, and Peace) based on Natyshastra [12].

corpus is having 350 poems and 74807 poetic words. The issues in corpus creation are listed below. •

Ambiguity in defining all emotional keywords.

•

Recognizing emotion from sentences with no emotional keywords.

•

Lack of semantic and syntactic information for emotion recognition.

•

Identifying emotion from poems with no emotional keywords.

•

Creative writing such as figurative language, rhyme, poetic form

•

Emotion Intensity.

The corpus has a data size of 350 poems of 8 leading poets of the period 1850-1950. The average words/ poem across the eight poets ranges from 74-284. B. Algorithm 1) Algorithm Search Input: Token t and Corpus C Output: Emotion tags 1. count ← 0 ; // used to keep track of multiple tags 2. For each entity (e ∈ C ) do 2.1.

TABLE I.

‘RASA’ AND THEIR EQUIVALENT ENGLISH EMOTION TYPES NatyaShastra ‘ Rasa’

Emotion

Shringara

Love

Hasya

Joy

Adbhutha

Surprise

Shantha

Peace

Roudra

Anger

Veera

Courage

Karuna

Sad

Bhibatsa

Hate

Bhyanakam

Fear

III.

3

← true ;

2.1.2

t . found

2.1.3

count ← count + 1;

return (t)

2) Algorithm Emotion-Tagging Input: Given poem P, Corpus C Output: Emotion tag et assigned to P 1.

Initialize the token array of P as T ← {t1 , t 2 ,......., t n } ,

2.

n tokens. Initialize the stop word array as S ← {s1 , s 2 ,......., s k } ,

3.

k stop words. Initialize negation word array as N ← n1 , n2 ,......, n j

METHODOLOGY

A. PERC Corpus Details The primary objective of this work is to create a corpus called PERC (Poem Emotion Recognition Corpus).It is classified under nine basic emotions such as Joy, Sad, Love, Hate, Anger, Peace, Courage, Fear, Surprise are described in Navarasa defined in Natyashastra[12] and to recognize emotions from poems. As we are not conscious of any publicly available poetry database, we created our own by mining public websites. The poem database made up poems extracted from the following websites. We collected 350 poems by eight poets. The

if (e . word = t )then t .tag [count ] ← e.tag ; 2.1.1

{

, j negation words. 4. For each ( t ∈ T ) do // Stop word removal from T 4.1 For each (s ∈ S ) do 4.1.1

If (t = s )then

4.1.1.1 5.

T ← T − {t} ;

For each ( t ∈ T ) do t ← search(t )

5.1


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}


If (t. found ← false) then

5.2

5.2.1

For each (n ∈ N ) and do

5.2.1.1

If (n = t ) then

5.2.1.1.1 5.2.1.1.2

t. found ← true ;

q ← search(t i +1 ) // search the

next word emotion in Corpus ti+1 is the next word 5.2.1.1.3 t.tag ← q.tag 5.2.1.1.4 Break; 5.2.2 if (t. found ← false) // the word is not a negation and search in word net 5.2.2.1 t.synonym ← wordnet (t ) 5.2.2.2

q ← search(t.synonym)

5.2.2.3

t.tag ← q.tag

5.2.2.4 6.

insert (C , t )

For each (t.tag [i ]∈ t ) do

⎞ × P(t.tag[i]) P⎛⎜ word ⎟ t tag i . [ ] ⎝ ⎠ t tag i . [ ] ⎤← 6.1 P ⎡ word ⎥⎦ ⎢⎣ P(word )

7. 8. 9.

⎛ ⎤⎞ et ← max⎜ P ⎡t.tag[i] word ⎥⎦ ⎟⎠ ⎝ ⎢⎣ t.emotion ← et.tag[i]

Return (et )

10. For each (t ∈ T ) 10.1 For each ( t .emotion ∈ Emotion ) 10.1.1 t .emotion = e 10.1.2 e.count = e.count + 1

emotion ← max( e .count ) 11. Return (emotion) 10.2

Description of ‘emotion tagging algorithm’ is as follows. This emotion corpus has manually tagged poetic words. Poems of different emotions are collected, and these poems are tokenized and stop words are removed. Stop words are usually the most frequent words, including articles, auxiliary verbs, prepositions, conjunctions, etc. Stop-word list composed of negative verbs such as not, is not, does not, do not, should not, etc. cannot be removed. There are publicly available stop-word lists consisting of most frequent words in a given language. Therefore we created a stop-words list, which does not contribute to emotion recognition The words such as doesn’t, shouldn’t, won’t be converted into does not, should not will not. etc. We have negation word list. Those words are tagged as . We are stemming the tokens as the words in different tense have different emotions. Other tokens are manually tagged using emotion tags such as , , , , , , , , .

Example 1) 2) 3) 4)

I hate chocolates. I hated chocolates. I am a Fearless person I do not love him.

Here the three sentences have different emotions. Sentence (1) tells that still hate chocolates, which means the emotion represented here is hate. However, the second sentence says that once he hated chocolate. So we do not know the emotion at present, which means we have to find out it by contextual information. Therefore, we tagged hate as and hated as . Sentence (3) depicts ‘courage’ only if the word ‘fearless’ is not considered for stemming process. Sentence (4) represents the emotion of hate or anger depends on the context. After the preprocessing stage, the tokens are searched in PER corpus and emotion of each word is identified and tagged using ‘search algorithm.' The emotion tagging of words might result in partial tagging of emotion words. If tokens are not found in the corpus (untagged token), that token is a new word. In that case, that particular word is searched in ‘Wordnet’ to find the synonym of that word. Then that synonym is searched in PERC to assign a tag for that word. That word and its emotion tag are added to the corpus. Sometimes the words can have more than one tag. At this stage, we used ‘Naïve Bayes Learning’ is used for tagging such words. 3) Prior Probability

P (Emotion

i

)=

9

N Emotion i

∑N

Emotion

i =1

(1) i

4) Constructing Unigram Language Model (add one smoothing) P(wordi | Emotioni ) = (Twordi | Emotioni + 1)

n

∑ (T j =1

wordi

| Emotioni + 1) (2)

5) Likelihood probability ( Applying Unigram Language Model) n

P( Poem | Emotioni ) = ∏ ( wordinPoem j | Emotioni ) (3) j =1

6) Posterior Probability P ( Emotion i | Poem ) = P ( Poem | Emotion i ) × P ( Emotion i ) P ( Poem )

(4) 7) Maximum Posterior Probability P ( Emotion i | Poem ) = arg max P ( Poem | Emotion i ) × P ( Emotion i ) i∈(1 .. 9 )

(5) When tags are observed in the tag set of a given poem, bigram model is subjected to handle negation. tagged word and its immediate successor words are observed. If the immediate successor word represents an emotion, then its opposite emotion is swapped Example: Not hate= love


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IV.

RESULT AND DISCUSSION

TABLE II.

CORPUS DETAILS

S.No

Poet

No. of poems

No of lines

No of words

No. of Content words

1

Rabindranath Tagore

186

5556

46099

4501

2

Sarojini Naidu

48

2538

13617

1858

3

Aurobindo

40

817

4809

1423

4

JidduKrishnamurthi

15

498

2496

885

5

Ananda Murthy

14

221

1642

711

6

Lalan

15

330

1116

471

7

Darshan Singh

15

432

2631

1092

8

Nazrul-Islam

17

527

2397

999

Total

350

TABLE III.

STATISTICS DETAILS OF CORPUS

Poet

No of Words

Unique word of Poet

more than once used unique word

Unused words

Average words/ poem

Average of unique word/poem

No. of Named Entities

Rabindranath Tagore

46099

2542

1027

0

249

14

6

Sarojini Naidu

13617

644

114

0

284

13

3

Aurobindo

4809

427

62

0

120

11

1

JidduKrishnamurthi

2496

183

8

6

166

12

1

Ananda Murthy

1642

102

10

5

117

7

1

Lalan

1116

78

6

8

74

5

0

Darshan Singh

2631

243

15

1

175

16

0

Nazrul-Islam

2397

181

16

0

141

11

1

TABLE IV.

RANK OF MAXIMUM USED(WORD-WISE) BY EACH POET

Rank

Rabindranath Tagore

Aurobindo

Sarojini Naidu

Ananda Murthy

Dharshan Singh

Lalan

Jiddu Krishnamurthi

Nazrul- Islam

I

when

Heart

Love

move

eyes

Moon

come

Mother

II

come

Who

Heart

love

heart

Black

clear

Love

III

heart

Life

Life

path

beloved

Flashing

jungle

Come

IV

Like

Light

Song

closer

light

Beauty

liberation

Child

V

Love

Mind

Dream

garland

friend

Eyes

love

Like


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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500 TABLE V.

RANK OF MAXIMUM OCCURRED WORDS IN EACH EMOTION

Rank

Anger

Courage

Fear

Hate

Joy

Love

Peace

Sad

Surprise

I

Anger

Dreams

running

Sorry

laughter

Love

Peace

grief

surprise

II

Love

Believe

stay

Hate

Day

Heart

world

son

morning

III

Pain

Love

fear

Love

Box

loving

human

house

ferocity

IV

Day

Courage

eyes

Thou

Idiot

honey

violence

brought

Rail

V

Inside

Yourself

sorry

Hear

Love

steady

energy

words

Fields

TABLE VI.

DETAILS OF POEM CATEGORIES

Emotions Anger Courage Fear Hate Joy Love Peace Sad Surprise

TABLE VII.

Emotion

Actual

No. of poems 21 15 17 15 55 89 51 70 17 350

EMOTION RECOGNITION RESULTS BY NAÏVE BAYES CLASSIFIER

Correctly Identified Poems

Anger

courage

fear

Hate

Joy

love

peace

Sad

Surprise

Wrongly Identified Poems

Anger

21

15

15

4

0

1

0

0

0

1

0

Courage

15

9

1

9

0

1

0

0

2

2

0

Fear

17

10

3

0

10

0

0

2

0

2

0

Hate

15

10

3

1

0

10

0

0

0

1

0

Joy

55

48

0

2

1

0

48

3

1

0

0

Love

89

73

1

5

0

0

8

73

2

0

0

Peace

51

40

0

3

0

0

3

4

40

1

0

Sad

70

54

4

0

1

0

1

10

0

54

0

Surprise

17

10

0

2

0

0

3

2

0

0

10

350

269

Table II shows the details of corpus such as poet’s name, the number of poems by each poet, the total number of lines, the total number of words and content words. Content words are words which give emotion after removing the stop-words. Observe that the number of content words contributes towards emotion recognition rather than an overall number of words or lines in the poem. In Table III, we analyze the author statistics. The primary attribute of author statistics includes unique words that are words used only by that particular poet which are recorded. For

instance, Tagore used unique words than others as his collection of poems are high. But notice that the average unique word per poem is greater for Darshan Singh. Unused words are nothing but the words that are not used by the particular poet but commonly used by others. Finally, the last attribute represents named entities (NE). Most of the NE includes God’s names such as Krishna, Radha, etc. Table IV gives the details of words specifically used many times by each poet summarized for all poets. For example, the


40


Table VII gives the details of emotion recognition result by ten cross folding naïve Bayes classification method. Table column head ‘Actual’ and ‘correctly’ gives the particulars of the correctly identified poem in a total number of poems in each emotion categories. Other column heads say the number of poems misclassified into other categories. The misclassifications of poems are due to ambiguity. Accuracy Measure For Naive Bayes Classifier 1

Precision Comparison Precision measure

word ‘love’ is used by five poets. Four poets use the word 'heart'. The word ‘come’ is used by three poets and the word ‘like’ is used by two poets. Table V gives the information of most occurred used in each emotion, and Table VI depicts the details of the number of poems occur in the corpus.

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Naïve

corpus

VSM

VSM-I

VSM-II

Anger

0.56

0.37

0.42

0.35

0.42

Courage

0.35

0.22

0.3

0.25

0.26

Fear

0.71

0.3

0.37

0.29

0.3

Hate

0.83

0.45

0.53

0.45

0.45

Joy

0.56

0.64

0.74

0.72

0.77

Love

0.78

0.65

0.76

0.74

0.78

Peace

0.89

0.77

0.87

0.8

0.91

Sad

0.89

0.69

0.76

0.74

0.77

1

0.83

1

1

1

Surprise

0.9

measure

0.8

Methods

0.7 0.6

Anger Hate Peace

0.5

Courage Joy Sad

Fear Love Surprise

0.4 0.3

Fear

Hate

Joy

Love

Peace

Sad

precision

Anger Courage 0.56

0.35

0.71

0.83

0.76

0.78

0.89

0.89

Surprise 1

recall

0.71

0.6

0.59

0.67

0.87

0.82

0.78

0.77

0.59

accuracy

0.95

0.93

0.97

0.98

0.94

0.89

0.95

0.93

0.98

Figure2. Comparison of Precision measures

Emotions recall

Recall Comparison accuracy

Figure1. Precision, Recall and Accuracy measure

Figure 1 depicts the precision-recall and accuracy measure of emotion recognition by Naïve Bayes Classifier. The accuracy of emotion recognition by Naïve Bayes classifier has more than 90% for all the emotion categories. Given to understand that to the best of our knowledge, we do not know of any other work in emotion recognition in poems. Hence our comparisons are of our previous works [10][11]. Emotion recognition by Naïve Bayes Classifier is compared with Probabilistic Corpus-based method [11] and Vector Space Model [10]. In the reference [10] we have changed few steps and modified into two versions as VSM-I and VSM-II. In VSM-I approach, we eliminated the stop-word removal and stemming process of each word in poems. In VSM-II approach, we created our own stop-word list that is a list of emotionless words (words does not contribute to emotion recognition). Usual components like negation words (not, never.., etc.), interrogative words (who, when.., etc.) are eliminated from the emotionless word list, as it contributes to emotion recognition process.

0.9

Recall Measure

precision

0.8 0.7 0.6 0.5 0.4 0.3

Naïve

corpus

VSM

VSM-I

VSM-II

Anger

0.71

0.54

0.63

0.58

0.56

Courage

0.6

0.43

0.53

0.45

0.5

Fear

0.59

0.43

0.53

0.45

0.48

Hate

0.67

0.45

0.56

0.48

0.5

Joy

0.87

0.66

0.76

0.71

0.86

Love

0.82

0.67

0.75

0.72

0.77

Peace

0.78

0.61

0.71

0.66

0.71

Sad

0.77

0.61

0.7

0.66

0.68

Surprise

0.59

0.43

0.5

0.48

0.5

Methods Anger Hate Peace

Courage Joy Sad

Fear Love Surprise

Figure3. Comparison of Recall measures


41


except probabilistic corpus-based approach. In this approach ‘Love’ Classification gets a higher value than all approaches. Naïve Bayes classifier gets better recall value for all emotion categories.

Accuracy comparison

Accuracy Measure

Naïve 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

corpus

VSM

VSM-I

VSM-II

Figure 3 and Figure 4 depicts the accuracy and F-measure of all approaches. Naïve Bayes classifier yields greater accuracy and F-measure while comparing with other methods. V. Anger

Coura ge

Fear

Hate

Joy

Love

Peace

Sad

Surpri se

Naïve

0.95

0.93

0.97

0.98

0.94

0.89

0.95

0.93

0.98

corpus

0.89

0.87

0.9

0.93

0.85

0.79

0.89

0.83

0.96

VSM

0.91

0.92

0.93

0.95

0.91

0.87

0.94

0.89

0.97

VSM-I

0.89

0.89

0.9

0.93

0.89

0.84

0.91

0.87

0.97

VSM-II

0.91

0.9

0.9

0.94

0.94

0.88

0.94

0.88

0.97

CONCLUSION AND FUTURE WORK

We have created a corpus PERC based on ‘Navarasa’ described in ‘Natyashastra’. Human experts created the Corpus. The corpus is consisting poems of eight Indian Poets at the period of 1850-1950.We are getting good accuracy measure. We compared with existing works and found that naïve Bayes classifier yields better accuracy measure. Some poems misclassified due to ambiguity. Two ways can resolve it. One is increasing the corpus size. Secondly considering the other features like semantic features and poetic features.

Emotions

Figure4. Comparison of Accuracy measure

REFERENCES

F-measure Comparison

[1]

F-measure

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Anger

Courag e

Fear

Hate

Joy

Love

Peace

Sad

Surpris e

Naïve

0.63

0.44

0.65

0.74

corpus

0.43

0.29

0.36

0.45

0.81

0.8

0.83

0.82

0.74

0.65

0.66

0.68

0.65

VSM

0.5

0.38

0.43

0.57

0.54

0.75

0.76

0.78

0.73

VSM-I

0.51

0.35

0.67

0.46

0.56

0.72

0.72

0.75

0.72

VSM-II

0.47

0.33

0.65

0.39

0.49

0.7

0.71

0.73

0.69

0.62

Emotions Naïve

corpus

VSM

Figure5. Comparison of F-measure

Figure 2 gives the details of precision measures among all the methods. Other than probabilistic corpus-based method, all other methods obtained a precision value as ‘1’. In the corpusbased method, it checked for the exact word matching and classified. Figure 3 shows the recall measures of all methods. ‘Joy’ classification obtains higher recall value in all the approaches

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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500 [12] M. Ghosh, The Natya Shastra (English Translation), vol.1 (Chapters IXXVII). The Royal Asiatic Society of Bengal: Calcutta, 1950.

AUTHORS PROFILE

P. S. Sreeja is the corresponding author of this paper. She is currently pursuing research in the Department of Computer Science and Engineering at Anna University, Chennai. She Completed her MCA from Bharathidasan University, Trichy, India, and M.Phil. (Computer Science) From the University of Madras, Chennai, India. She is a First rank holder in M.Phil. Her research interests are Artificial Intelligence, Text Mining, and Natural Language Processing.

G. S. Mahalakshmi is an Assistant Professor (Senior Grade) in the Department of Computer Science and Engineering, College of Engineering, Anna University, Chennai. She completed her B.E. (Computer Science and Engineering) from R.V.S. College of Engineering and Technology, Dindigul and M.E. (Computer Science and Engineering) and Ph.D. from College of Engineering, Anna University, Chennai. She has numerous international journal and conference publications to her credit. She is also the author of Tamil Edition of B.E. course – text books - Fundamentals of Computing and Computer Practice of Anna University. She has authored many book chapters and derived 100+ citations to her credit. Her research interests include Reasoning, Knowledge Sharing and representation, Text Mining, Social Network Analysis, bibliometrics, and Natural Language Computing.


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