Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/7783
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dc.contributor.authorSachin, Kumar, S.
dc.contributor.authorAnand, Kumar, M.
dc.contributor.authorSoman, K.P.
dc.contributor.authorPoornachandran, P.
dc.date.accessioned2020-03-30T10:02:48Z-
dc.date.available2020-03-30T10:02:48Z-
dc.date.issued2020
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2020, Vol.910, , pp.1-15en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7783-
dc.description.abstractSentiment analysis (SA) or polarity identification is a research topic which receives considerable number of attention. The work in this research attempts to explore the sentiments or opinions in text data related to any event, politics, movies, product reviews, sports, etc. The present article discusses the use of dynamic modes from dynamic mode decomposition (DMD) method with random mapping for sentiment classification. Random mapping is performed using random kitchen sink (RKS) method. The present work aims to explore the use of dynamic modes as the feature for sentiment classification task. In order to conduct the experiment and analysis, the dataset used consists of tweets from SAIL 2015 shared task (tweets in Tamil, Bengali, Hindi) and Malayalam languages. The dataset for Malayalam is prepared by us for the work. The evaluations are performed using accuracy, F1-score, recall, and precision. It is observed from the evaluations that the proposed approach provides competing result. � Springer Nature Singapore Pte Ltd. 2020.en_US
dc.titleDynamic mode-based feature with random mapping for sentiment analysisen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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