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dc.contributor.authorCHOWDHURY, ANUSHA
dc.date.accessioned2018-10-23T13:21:45Z
dc.date.available2018-10-23T13:21:45Z
dc.date.issued2018-05-30
dc.identifier.otherCHOWDHURY_cornell_0058O_10280
dc.identifier.otherhttp://dissertations.umi.com/cornell:10280
dc.identifier.otherbibid: 10489407
dc.identifier.urihttps://hdl.handle.net/1813/59323
dc.description.abstractIn this work, we explore different techniques to extract opinions, sentiments and beliefs from text. In particular, we look at neural network based approaches since deep learning has gained wide popularity nowadays and is known to perform effectively for the kind of problems we are looking at. The first goal was to solve the BeSt (Belief and Sentiment) Evaluation task from the 2016 Text Analysis Conference. Here we used bidirectional Long Short Term Memory networks (LSTMs) for sentiment detection in a given document. We looked with particular interest at the sentiment polarity to find out whether it is positive, negative or neutral sentiment. Our neural network based model consists of a multi-layered bidirectional LSTM which takes as input embeddings for the sequence of words in a given sentence along with annotated source and target, and outputs probabilities for the different sentiment polarities. We evaluated the model using standard precision, recall and F1 scores and then compared our results to scores in the existing literature. The second goal was to identify opinion expressions along with their sources and targets in a given text. We compared a simple baseline approach, support vector machine and conditional random field to a feedforward neural network and a bidirectional LSTM approach. All approaches were evaluated on a standard dataset from the sentiment analysis literature - the MPQA dataset.
dc.language.isoen_US
dc.subjectbelief
dc.subjectInformation science
dc.subjectComputer science
dc.subjectneural network
dc.subjectConditional Random Field
dc.subjectLSTM
dc.subjectOpinion
dc.subjectSentiment
dc.titleSENTIMENT, BELIEF AND OPINION DETECTION USING NEURAL NETWORKS
dc.typedissertation or thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorCornell University
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Computer Science
dc.contributor.chairCardie, Claire T.
dc.contributor.committeeMemberMimno, David
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/X4GB229P


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