Evaluating N-gram based evaluation metrics for automatic keyphrase extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Selecting Attributes for Sentiment Classification Using Feature Relation Networks
IEEE Transactions on Knowledge and Data Engineering
A Hidden Topic-Based Framework toward Building Applications with Short Web Documents
IEEE Transactions on Knowledge and Data Engineering
Automatically extracting polarity-bearing topics for cross-domain sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Online conversation mining for author characterization and topic identification
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
Weakly Supervised Joint Sentiment-Topic Detection from Text
IEEE Transactions on Knowledge and Data Engineering
A Supervised Framework for Keyword Extraction From Meeting Transcripts
IEEE Transactions on Audio, Speech, and Language Processing
Detecting implicit expressions of emotion in text: A comparative analysis
Decision Support Systems
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Various kinds of audio and video data are generated everyday like audio and video chatting, blog posts, e-communities, social networks, customer reviews on wide range of products and online audio and video helpline for different technical problems. Providing keywords for these audio files, thus allow the users to quickly grab the gist of the lengthy recordings and helps information access effectively. Nowadays online reviews are having greater impact on consumers and companies compared to the traditional data. New methodologies are available for automated sentiment analysis and discovering the hidden knowledge from unstructured audio and video data. Among various sentiment analysis tasks, one of them is sentiment classification, ie., identifying whether the input of the given text is positive or negative. In this paper it is proposed to combine both keyword extraction and sentiment classification into a single model which will perform both the works at a single time.