Manju Venugopalan currently serves as Assistant Professor at the department of Computer Science, Amrita School of Engineering, Bengaluru. She is currently pursuing her Ph.D. Her areas of research include Natural Language Processing, Machine Learning, Algorithms and Database Management Systems.


Publication Type: Conference Paper
Year of Publication Publication Type Title
2016 Conference Paper D. Mishra, Venugopalan, M., and Deepa Gupta, “Context Specific Lexicon for Hindi Reviews”, in Procedia Computer Science, 2016, vol. 93, pp. 554 - 563.[Abstract]

In the era of social networking, immense amount of posts, comments and tweets generated every second are increasing the size of social database. The analysis of this voluminous data is necessary for exploring the orientation of people's opinion about a particular entity. Most of the online data are in English language, but due to increase in technology and improved awareness of people, the online data available in Indian languages are gradually increasing. Sentiment analysis of English language alone is not sufficient to know the inclination of people towards an entity, other Indian language sentiment analysis is a must, their contribution is also important for us. The available sentiment classification lexicon resources like Hindi SentiWordNet are generic in nature and hence results in average sentiment classification accuracy due to contextual dependency. To improve the sentiment classification accuracy, we present an improvised lexicon resource for Hindi language for Hotel and Movie domains. The improvised polarity lexicon has been built reflecting context sensitivity and to increase coverage it has been expanded used synonyms based approach. The built polarity lexicon resource showcases an improvement in accuracy of 42% and 78% in Movie and Hotel domain, respectively, compared to the existing Hindi SentiWordNet lexicon resource. More »»
2015 Conference Paper M. Venugopalan and Deepa Gupta, “Exploring sentiment analysis on Twitter data”, in Eighth International Conference on Contemporary Computing (IC3), 2015.[Abstract]

The growing popularity of microblogging websites has transformed these into rich resources for sentiment mining. Even though opinion mining has more than a decade of research to boost about, it is mostly confined to the exploration of formal text patterns like online reviews, news articles etc. Exploration of the challenges offered by informal and crisp microblogging have taken roots but there is scope for a large way ahead. The proposed work aims at developing a hybrid model for sentiment classification that explores the tweet specific features and uses domain independent and domain specific lexicons to offer a domain oriented approach and hence analyze and extract the consumer sentiment towards popular smart phone brands over the past few years. The experiments have proved that the results improve by around 2 points on an average over the unigram baseline.

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2015 Conference Paper M. Venugopalan and Deepa Gupta, “Sentiment Classification for Hindi Tweets in a Constrained Environment Augmented Using Tweet Specific Features”, in Mining Intelligence and Knowledge Exploration, 2015, pp. 664–670.[Abstract]

India being a diverse country rich in spoken languages with around 23 official languages has always left open a wide arena for NLP researchers. The increase in the availability of voluminous data in Indian languages in the recent years has prompted researchers to explore the challenges in the Indian language domain. The proposed work explores Sentiment Analysis on Hindi tweets in a constrained environment and hence proposes a model for dealing with the challenges in extracting sentiment from Hindi tweets. The model has exhibited an average performance with cross validation accuracy for training data around 56 % and a test accuracy of 43 %.

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Publication Type: Journal Article
Year of Publication Publication Type Title
2015 Journal Article M. Venugopalan and Deepa Gupta, “An Enhanced Polarity Lexicon by Learning-based Method Using Related Domain Knowledge”, International Journal of Information Processing and Management, vol. 6, no. 2, pp. 61–72, 2015.[Abstract]

The inborn human instinct to know what others think has contributed to the growing popularity of Sentiment Analysis. Sentiment in a text is mostly derived from opinion oriented words or lexicons. But the challenge is put forward by opinion oriented words which are domain specific. Various researchers have proposed methods to improve the polarity scores of these domain specific lexicons. Existing works utilize mainly single domain knowledge which is not sufficient to update a domain-specific lexicon. The proposed work attempts a domain adaptation model by building a polarity lexicon using knowledge from multiple related domains. The polarity lexicon thus built when tested on new domains provides fairly good classification results thus implementing true domain adaptation. The proposed approach has been tested on Amazon product reviews from ten related domains which include Printer, Scanner, MP3 Player, iPod, LCD TV etc. A significant improvement in accuracy ranging from 1 to 14.5 points on learned domains and 0.5 to 8 points across new domains over the baseline has been observed.

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