Publications

  • Shubham Gautam, Pushpak Bhattacharyya
    Proceedings of the Ninth Workshop on Statistical Machine Translation; 06/2014
  • Proceedings of the Ninth Workshop on Statistical Machine Translation; 06/2014
  • Aditya Joshi, Pushpak Bhattacharyya, Abhijit Mishra
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    ABSTRACT: (To be published)
    Association For Computational Linguistics Conference 2014; 01/2014
  • Abhijit Mishra, Pushpak Bhattacharyya, Michael Carl
    Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers); 08/2013
  • Ankit Ramteke, Akshat Malu, Pushpak Bhattacharyya, Saketha J Nath
    Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers); 08/2013
  • Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers); 08/2013
  • Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations; 08/2013
  • Anoop Kunchukuttan, Ritesh Shah, Pushpak Bhattacharyya
    Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task; 08/2013
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    Subhabrata Mukherjee, Pushpak Bhattacharyya
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    Subhabrata Mukherjee, Pushpak Bhattacharyya
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    Subhabrata Mukherjee, Pushpak Bhattacharyya
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    Subhabrata Mukherjee, Pushpak Bhattacharyya
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    Subhabrata Mukherjee, Pushpak Bhattacharyya
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    ABSTRACT: : In this paper, we present a novel approach to identify feature specific expressions of opinion in product reviews with different features and mixed emotions. The objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Capitalizing on the view that more closely associated words come together to express an opinion about a certain feature, dependency parsing is used to identify relations between the opinion expressions. The system learns the set of significant relations to be used by dependency parsing and a threshold parameter which allows us to merge closely associated opinion expressions. The data requirement is minimal as this is a one time learning of the domain independent parameters. The associations are represented in the form of a graph which is partitioned to finally retrieve the opinion expression describing the user specified feature. We show that the system achieves a high accuracy across all domains and performs at par with state-of-the-art systems despite its data limitations.
    Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I; 06/2013
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    Subhabrata Mukherjee, Pushpak Bhattacharyya
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    ABSTRACT: We propose a lightweight method for using discourse relations for polarity detection of tweets. This method is targeted towards the web-based applications that deal with noisy, unstructured text, like the tweets, and cannot afford to use heavy linguistic resources like parsing due to frequent failure of the parsers to handle noisy data. Most of the works in micro-blogs, like Twitter, use a bag-of-words model that ignores the discourse particles like but, since, although etc. In this work, we show how the discourse relations like the connectives and conditionals can be used to incorporate discourse information in any bag-of-words model, to improve sentiment classification accuracy. We also probe the influence of the semantic operators like modals and negations on the discourse relations that affect the sentiment of a sentence. Discourse relations and corresponding rules are identified with minimal processing - just a list look up. We first give a linguistic description of the various discourse relations which leads to conditions in rules and features in SVM. We show that our discourse-based bag-of-words model performs well in a noisy medium (Twitter), where it performs better than an existing Twitter-based application. Furthermore, we show that our approach is beneficial to structured reviews as well, where we achieve a better accuracy than a state-of-the-art system in the travel review domain. Our system compares favorably with the state-of-the-art systems and has the additional attractiveness of being less resource intensive.
    COLING 2012, 24th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 8-15 December 2012, Mumbai, India; 06/2013
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    Subhabrata Mukherjee, Pushpak Bhattacharyya
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    ABSTRACT: This paper describes a weakly supervised system for sentiment analysis in the movie review domain. The objective is to classify a movie review into a polarity class, positive or negative, based on those sentences bearing opinion on the movie alone, leaving out other irrelevant text. Wikipedia incorporates the world knowledge of movie-specific features in the system which is used to obtain an extractive summary of the review, consisting of the reviewer’s opinions about the specific aspects of the movie. This filters out the concepts which are irrelevant or objective with respect to the given movie. The proposed system, WikiSent, does not require any labeled data for training. It achieves a better or comparable accuracy to the existing semi-supervised and unsupervised systems in the domain, on the same dataset. We also perform a general movie review trend analysis using WikiSent.
    Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I; 06/2013
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    ABSTRACT: In this paper, we introduce a new WordNet based similarity metric, SenSim, which incorporates sentiment content (i.e., degree of positive or negative sentiment) of the words being compared to measure the similarity between them. The proposed metric is based on the hypothesis that knowing the sentiment is beneficial in measuring the similarity. To verify this hypothesis, we measure and compare the annotator agreement for 2 annotation strategies: 1) sentiment information of a pair of words is considered while annotating and 2) sentiment information of a pair of words is not considered while annotating. Interannotator correlation scores show that the agreement is better when the two annotators consider sentiment information while assigning a similarity score to a pair of words. We use this hypothesis to measure the similarity between a pair of words. Specifically, we represent each word as a vector containing sentiment scores of all the content words in the WordNet gloss of the sense of that word. These sentiment scores are derived from a sentiment lexicon. We then measure the cosine similarity between the two vectors. We perform both intrinsic and extrinsic evaluation of SenSim and compare the performance with other widely used WordNet similarity metrics.
    n Proceedings of the International Conference on Global Wordnets (GWC 2011), Matsue, Japan, Jan, 2012; 06/2013
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    ABSTRACT: In this paper, we present TwiSent, a sentiment analysis system for Twitter. Based on the topic searched, TwiSent collects tweets pertaining to it and categorizes them into the different polarity classes positive, negative and objective. However, analyzing micro-blog posts have many inherent challenges compared to the other text genres. Through TwiSent, we address the problems of 1) Spams pertaining to sentiment analysis in Twitter, 2) Structural anomalies in the text in the form of incorrect spellings, nonstandard abbreviations, slangs etc., 3) Entity specificity in the context of the topic searched and 4) Pragmatics embedded in text. The system performance is evaluated on manually annotated gold standard data and on an automatically annotated tweet set based on hashtags. It is a common practise to show the efficacy of a supervised system on an automatically annotated dataset. However, we show that such a system achieves lesser classification accurcy when tested on generic twitter dataset. We also show that our system performs much better than an existing system.
    Proceedings of the 21st ACM international conference on Information and knowledge management; 06/2013
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    Subhabrata Mukherjee, Pushpak Bhattacharyya
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    ABSTRACT: In this paper, we propose a weakly supervised system, YouCat, for categorizing Youtube videos into different genres like Comedy, Horror, Romance, Sports and Technology The system takes a Youtube video url as input and gives it a belongingness score for each genre. The key aspects of this work can be summarized as: (1) Unlike other genre identification works, which are mostly supervised, this system is mostly unsupervised, requiring no labeled data for training. (2) The system can easily incorporate new genres without requiring labeled data for the genres. (3) YouCat extracts information from the video title, meta description and user comments (which together form the video descriptor). (4) It uses Wikipedia and WordNet for concept expansion. (5) The proposed algorithm with a time complexity of O(|W|) (where (|W|) is the number of words in the video descriptor) is efficient to be deployed in web for real-time video categorization. Experimentations have been performed on real world Youtube videos where YouCat achieves an F-score of 80.9%, without using any labeled training set, compared to the supervised, multiclass SVM F-score of 84.36% for single genre prediction. YouCat performs better for multi-genre prediction with an F-Score of 90.48%. Weak supervision in the system arises out of the usage of manually constructed WordNet and genre description by a few root words.
    COLING 2012, 24th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 8-15 December 2012, Mumbai, India; 06/2013
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    Subhabrata Mukherjee, Pushpak Bhattacharyya
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    ABSTRACT: Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a result there has been an eruption of interest in people to mine these vast resources of data for opinions. Sentiment Analysis or Opinion Mining is the computational treatment of opinions, sentiments and subjectivity of text. In this report, we take a look at the various challenges and applications of Sentiment Analysis. We will discuss in details various approaches to perform a computational treatment of sentiments and opinions. Various supervised or data-driven techniques to SA like Na\"ive Byes, Maximum Entropy, SVM, and Voted Perceptrons will be discussed and their strengths and drawbacks will be touched upon. We will also see a new dimension of analyzing sentiments by Cognitive Psychology mainly through the work of Janyce Wiebe, where we will see ways to detect subjectivity, perspective in narrative and understanding the discourse structure. We will also study some specific topics in Sentiment Analysis and the contemporary works in those areas.
    04/2013;

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