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Published: 23
DOI: https:/ / d
Citation: Sulta
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Copyright: © 2
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Abstract: As o
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generated dail
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DUCT REV
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Vol.08/ Issue0
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ODUCT REVIEW
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s, India
rms of the Crea
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EVIEW SENTIME
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goods (Sen, 20
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Pang, Lee and Va
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ANALYSIS
l.com
ENT ANALYSI
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2013). Thus, a
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In this study t
when bag of w
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well trained ma
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data set. Chaov
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y they tried va
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Volume 08 (July
--------------------
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various feature
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machine learn
different travel
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text review and
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SN: 2393-9842
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------------------
Page-137
performed bette
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been attempte
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the Naive Baye
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tter
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In English forma
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POStagger is a
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use the classifi
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on the reviews
as training da
transform the
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test data to me
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Fig 2:
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Issue 07, Volu
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ormat there are
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Volume 08 (July
--------------------
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are several stop
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ments, each clas
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top words. POS
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SN: 2393-9842
.com/ archives
------------------
Page-138
rts of speech t
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4. International Research Journal of Computer Science (IRJCS) ISSN: 2393-9842
Issue 07, Volume 08 (July 2021) https:/ / www.irjcs.com/ archives
---------------------------------------------------------------------------------------------------------------------------------------------------
© 2014-21, IRJCS-All Rights Reserved Page-139
It can be either Yes or No, 0 or 1, true or False, etc. but instead of giving the exact value as 0 and 1, it gives the
probabilistic values which lie between 0 and 1.Logistic Regression is much similar to the Linear Regression except
that how they are used. Linear Regression is used for solving Regression problems, whereas Logistic regression is
used for solving the classification problems. In Logistic regression, instead of fitting a regression line, we fit an "S"
shaped logistic function, which predicts two maximum values (0 or 1).The curve from the logistic function indicates
the likelihood of something such as whether the cells are cancerous or not, a mouse is obese or not based on its
weight, etc. Logistic Regression is a significant machine learning algorithm because it has the ability to provide
probabilities and classify new data using continuous and discrete datasets. Logistic Regression can be used to
classify the observations using different types of data and can easily determine the most effective variables used for
the classification. The below image is showing the logistic function: Naive Bayes is another machine learning
technique that is known for being powerful despite its simplicity. This classifier is based on Bayes theorem and
relies on the assumption that the features (which are usually words in text classification) are mutually independent.
In spite of the fact that this assumption is not true (because in some cases the order of the words is important),
Naïve Bayes classifiers have proved to perform surprisingly well . The first step that should be carried out before
applying the Naive Bayes model on text classification problems is feature extraction.
Feature Extraction
Bag of Words: Bag of word is a process of extracting features by representing simplified text or data, used in natural
language processing and information retrieval. In this model, a text or a document is represented as the bag
(multiple set) of its words. So, simply bag of words in sentiment analysis is creating a list of useful words. We have
used bag of words approach to extract our feature sets. After pre-processed dataset we used pos tagging to separate
different parts of speech and from that we select nouns and adjectives and use those to create a bag of words. Then
we run it through a supervised learning and find our results and also the top used words from the review dataset.
TF-IDF:TF-IDF is an information retrieval technique which weighs a term’s frequency (TF) and also inverse
document frequency (IDF). Each word or term has its own TF and IDF score. The TF and IDF product scores of a
term is referred to the TF*IDF weight of that term. Simply we can state that the higher the TF*IDF score (weight) the
rarer the term and vice versa. TF of a word is the frequency of a word.IDF of a word is the measure of how
significant that term is throughout the corpus.
When words do have high TF*IDF weight in content, content will always be amongst the top search results, so
anyone can:
1. Stop worrying about using the stop-words,
2. Successfully find words with higher search
Volumes and lower competition
Chi Square: Chi square (X^2) is a calculation that is used to determine how smaller the difference between the
observed data and the expected data. In this approach we have preprocessed our dataset then we have divided
data into training and testing set. We used pipeline method to apply TF-IDF, Chi square and other classifiers onto
our dataset and got the results.
Algorithm for proposed approach
Input:
Labeled Data = labeled data obtained after active learning process.
Output:
Accuracy of classifiers;
/ / product review polarity accuracy
1. Load labeled data positive & negative
2. Preprocesse dlabeled data
3. for every X= {X1…Xn} in labeled data
4. Extractfeature(Xi)
5. Cross validate into training & testing set
6. Classifier.train()
7. Classifier(testing set)
8. Accuracy= classifier.accuracy()
9. show result(accuracy)
10.end

5. --
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ines and other
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SN: 2393-9842
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ng dataset. Fig.
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42
es
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also
ine,
five
the
has
hra
l to
ised
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sed
ing
for
ural