Sentiment classification is a special case of text
classification whose aim is classification of a given text
according to the sentimental polarities of opinions it contains,
that is favorable or unfavorable, positive or negative. Student
feedback is collected as response to set of positive and
negative questions. The idea is to identify and extract the
relevant information from feedback questions in natural
language texts in order to determine a set of best predictive
attributes or features for classification of unlabelled
opinionated text. Sentiment classification is used for training
a binary classifier using feedback questions annotated for
positive or negative sentiment and evaluates the
corresponding feedback received. This paper compares the
sentiment classification of student feedback questions at
sentence level and at token level for different classifiers.
Chandrika Chatterjee : is pursuing Masters of Technology in
Computer Science & Engg. Department, From National
Institute of Technology, Agartala (Tripura). She has completed her
Bachelor of Engineering in Computer Science and Engg.
Department, from Gurunanak Institute of Technology, Kolkata (West
Bengal) under West Bengal University of Technology. Her field of
interest is focused on data mining, Natural Language Processing,
Machine Learning and Evolutionary Computing.
Kunal Chakma : is working as Assistant Professor in Computer
Science & Engg. Department, National Institute of
Technology, Agartala (Tripura). He has completed Masters of
Technology in Computer Science & Engg. Department, from
National Institute of Technology, Agartala (Tripura). His field of
interest is focused on Data mining, Natural Language
Processing, Systems Software, Operating Systems.
Sentiment Analysis
Tokens
Classification
Support
Vector Machine
Decision Tree
In the comparison, we see that token level sentiment
analysis for decision tree based classifier gives improved
results. Decision tree builds classification models or
regression models in form of tree structure. It decomposes
a dataset into smaller subsets while at the same time an
associated decision tree is incrementally developed. The
final result is a tree with decision nodes and leaf
nodes. Decision tree based classifiers are very flexible and
addresses every possible failure, however failure for a
system concerns small set of symptoms that are relevant to
the system at hand. However SVM and Naïve Bayes
classifier gives poor results in token level classification.
Changing the feature set we can analyze the different
accuracy levels of sentence level sentiment analysis and
token level sentiment analysis. Although the carefully
chosen corpus in training set for training the classifier
contained fifty percent positive and negative classes yet
we observe a very good recall of negative class but very
poor recall of positive class. This biased classification
should be analyzed.
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