Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Kamath S․, S.S.Subject: search.filters.subject.Natural language processing

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    Sentiment analysis based approaches for understanding user context in Web content
    (2013) Kamath S․, S.S.; Bagalkotkar, A.; Khandelwal, A.; Pandey, S.; Poornima, K.
    In our day to day lives, we highly value the opinions of friends in making decisions about issues like which brand to buy or which movie to watch. With the increasing popularity of blogs, online reviews and social networking sites, the current trend is to look up reviews, expert opinions and discussions on the Web, so that one can make an informed decision. Sentiment analysis, also known as opinion mining is the computational study of opinions, sentiments and emotions expressed in natural language for the purpose of decision making. Sentiment analysis applies natural language processing techniques and computational linguistics to extract information about sentiments expressed by authors and readers about a particular subject, thus helping users in making sense of huge volume of unstructured Web data. Applications like review classification, product review mining and trend prediction benefit from sentiment analysis based techniques. This paper presents a study of different approaches in this field, the state of the art techniques and current research in Sentiment Analysis based approaches for understanding user's context. © 2013 IEEE.
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    A novel technique for efficient text document summarization as a service
    (2013) Bagalkotkar, A.; Khandelwal, A.; Pandey, S.; Kamath S․, S.S.
    Due to an exponential growth in the generation of web data, the need for tools and mechanisms for automatic summarization of Web documents has become very critical. Web data can be accessed from multiple sources, for e.g. on different Web pages, which makes searching for relevant pieces of information a difficult task. Therefore, an automatic summarizer is vital towards reducing human effort. Text summarization is an important activity in the analysis of a high volume text documents and is currently a major research topic in Natural Language Processing. It is the process of generation of the summary of an input document by extracting the representative sentences from it. In this paper, we present a novel technique for generating the summarization of domain-specific text from a single Web document by using statistical NLP techniques on the text in a reference corpus and on the web document. The summarizer proposed generates a summary based on the calculated Sentence Weight (SW), the rank of a sentence in the document's content, the number of terms and the number of words in a sentence, and using term frequency in the input corpus. © 2013 IEEE.
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    Query-oriented unsupervised multi-document summarization on big data
    (Association for Computing Machinery acmhelp@acm.org, 2016) Sunaina; Kamath S․, S.S.
    Real time document summarization is a critical need nowadays, owing to the large volume of information available for our reading, and our inability to deal with this entirely due to limitations of time and resources. Oftentimes, information is available in multiple sources, offering multiple contexts and viewpoints on a single topic of interest. Automated multi-document summarization (MDS) techniques aim to address this problem. However, current techniques for automated MDS suffer from low precision and accuracy with reference to a given subject matter, when compared to those summaries prepared by humans and takes large time to create the summary when the input given is too huge. In this paper, we propose a hybrid MDS technique combining feature based algorithms and dynamic programming for generating a summary from multiple documents based on user provided query. Further, in real-world scenarios, Web search serves up a large number of URLs to users, and the work of making sense of these with reference to a particular query is left to the user. In this context, an efficient parallelized MDS technique based on Hadoop is also presented, for serving a concise summary of multiple Webpage contents for a given user query in reduced time duration. © 2016 ACM.