THIS IS ABOUT AN MOVIE RATING,USING SENTIMENTAL KEYWORDS

1. Name: Manoj M
Reg.No:312319205088
Name: Navendhan S
Reg.No:312319205103

3. Recommender systems are information filtering tools that
aspire to predict the rating for users and items, predominantly
from big data to recommend their likes. Movie recommendation
systems provide a mechanism to assist users in classifying
users with similar interest.
Sentiment analysis studies the subjective information in an
expression, that is, the opinions, appraisals, emotions, or
attitudes towards a topic, person or entity. Expressions can be
classified as positive, negative, or neutral. For example: “I really
like the new design of your website!” → Positive

4. ABSTRACT
We usually come across movie rating websites where users are allowed to
rate and
comment on movies online. These ratings are provided as input to the
website admin.
The admin then checks reviews, critic’s ratings and displays an online rating
for every
movie. Here we propose an online system that automatically allows users to
post reviews
and store them. The system now analyzes this data to check for user
sentiments
associated with each comment. Our system consists of a sentiment library.
The system
breaks user comments to check for sentiment keywords. Once the keywords
are found it

5. Author, Journal
Year
Title Concept Pros and Cons
Kudakwashe
Zvarevashe,
Oludayo O
Olugbara
2018
“A Framework for
Sentiment
Analysis with
Opinion Mining of
MovieReviews”
The rapid increase in mountains of
unstructured textual data
accompanied by proliferation of
tools to analyse them has opened
up great opportunities and
challenges for text mining research.
[...] Key MethodThe proposed
framework is termed sentiment
polarity that automatically prepares
a sentiment dataset for training and
testing to extract unbiased
opinions of hotel services from
reviews. A comparative analysis was
established with NaïveBayes
multinomial, sequential minimal
optimization,compliment Naïve.

6. Author, Journal
Year
Title Concept Pros and Cons
Asiri Wijesinghe
October 2015
“Sentiment
Analysis on Movie
Reviews”
We propose a learning framework for graph
kernels, which is theoretically grounded on
regularizing optimal transport. This
framework provides a novel optimal
transport distance metric, namely
Regularized Wasserstein (RW) discrepancy,
which can preserve both features and
structure of graphs via Wasserstein distances
on features and their local varia.Entity
resolution targets at identifying records that
represent the same real-world entity from
one or more datasets. A major challenge in
learning-based entity resolution is how to
reduce the label cost for training.

7. Author, Journal
Year
Title Concept Pros and Cons
Zainab Mirza,
Mehwash Khan,
Saima Khan,
Khurshid Khatri
Month: April -
June 2015
“Movie Rating
System Based
On Opinion
Mining”
Opinion Mining (also referred as Sentiment
Analysis) refers to the use of natural
language processing, text analysis and
computational linguistics to identify and
extract subjective information in source
materials. For any type of information
gathering, we always see what the opinions
of people about that product are or any
service provided. With the growing
availability and popularity of opinion-rich
resources such as online review sites and
personal blogs, new opportunities and
challenges are now available for people.
Opinion Mining is such a field that helps user
know the reviews about any product or
service he / she is interested in. Opinion
Mining is a task of extracting from a

8. Author, Journal
Year
Title Concept Pros and Cons
Sagar Chavan,
Akash
Morwal,Shivam
Patanwala,Prachi
Janrao, 2017
“Sentiment
Analysis of Movie
Rating System”
Manual reading of contents of movie
reviews is a tedious and time-consuming
task for the movie viewers. So, they are not
able to make proper opinions/judgment to
watch movies or not. Hence, this is our
purpose to develop an automated movie
rating system based on sentiment analysis.
We have two sentiments which we are
showing using emojis- Happy for positive
sentiments and Sad for negative
sentiments. Keywords:
Sentiment, Sentiment Analysis, Movie
reviews, Human behaviour

9. Author,
Journal
Year
Title Concept Pros and Cons
Salvetti, F.,
Lewis, S.,
&
Reichenba
ch, C.
(2004).
Salvetti, F.,
Lewis, S., &
Reichenbach, C.
(2004).
One approach to assessing overall opinion
polarity (OvOP) of reviews, a concept defined in
this paper, is the use of supervised machine
learning mechanisms. In this paper, the impact
of lexical filtering, applied to reviews, on the
accuracy of two statistical classifiers (Naive
Bayes and Markov Model) with respect to OvOP
identification is observed. Two kinds of lexical
filters, one based on hypernymy as provided by
WordNet (Fellbaum, 1998), and one hand-
crafted filter based on part-of-speech (POS)
tags, are evaluated. A ranking criterion based
on a function of the probability of having
positive or negative polarity is introduced and
verified as being capable of achieving 100%
accuracy with 10% recall. Movie reviews are

10. Author,
Journal
Year
Title Concept Pros and Cons
Beineke,
P., Hastie,
Vaithyanat
han, S
(2004)
The
sentimental
factor:
Improving
review
classification
via human-
provided
information.
Sentiment classification is the task of labeling a
re- view document according to the polarity of
its pre- vailing opinion (favorable or
unfavorable). In ap- proaching this problem, a
model builder often has three sources of
information available: a small collection of
labeled documents, a large collection of
unlabeled documents, and human understanding
of language. Ideally, a learning method will
utilize all three sources. To accomplish this goal,
we general- ize an existing procedure that uses
the latter two. We extend this procedure by re-
interpreting it as a Naive Bayes model for
document sentiment. Viewed as such, it can also
be seen to extract a pair of derived features that
are linearly combined to predict sentiment. This

11. Author,
Journal Year
Title Concept Pros and Cons
Maite
Taboada,
Julian Brooke,
Milan
Tofiloski,
Kimberly
Voll,
Manfred
Stede
June01 (2011)
Lexicon-
based
methods for
sentiment
analysis
We present a lexicon-based approach to extracting
sentiment from text. The Semantic Orientation
CALculator (SO-CAL) uses dictionaries of words
annotated with their semantic orientation (polarity
and strength), and incorporates intensification and
negation. SO-CAL is applied to the polarity
classification task, the process of assigning a
positive or negative label to a text that captures
the text's opinion towards its main subject matter.
We show that SO-CAL's performance is consistent
across domains and in completely unseen data.
Additionally, we describe the process of dictionary
creation, and our use of Mechanical Turk to check
dictionaries for consistency and reliability.

12. Author,
Journal Year
Title Concept Pros and Cons
Lin, Y., Zhang,
J., Wang, X.,
Zhou, A
(2012)
An
information
theoretic
approach to
sentiment
polarity
classification.
Sentiment classification is a task of classifying
documents according to their overall sentiment
inclination. It is very important and popular in
many web applications, such as credibility analysis
of news sites on the Web, recommendation system
and mining online discussion. Vector space model
is widely applied on modeling documents in
supervised sentiment classification, in which the
feature presentation (including features type and
weight function) is crucial for classification
accuracy. The traditional feature presentation
methods of text categorization do not perform
well in sentiment classification, because the
expressing manners of sentiment are more subtle.
We analyze the relationships of terms with
sentiment labels based on information theory, and
propose a method by applying information

13. Author,
Journal Year
Title Concept Pros and Cons
Pang, B., Lee.
L.,
Vaithyanathan
.
S(2002)
Sentiment
classification
using
machine
learning
techniques
We consider the problem of classifying documents
not by topic, but by overall sentiment, e.g.,
determining whether a review is positive or
negative. Using movie reviews as data, we find that
standard machine learning techniques definitively
outperform human-produced baselines. However,
the three machine learning methods we employed
(Naive Bayes, maximum entropy classification, and
support vector machines) do not perform as well
on sentiment classification as on traditional topic-
based categorization. We conclude by examining
factors that make the sentiment classification
problem more challenging.

14. Author,
Journal Year
Title Concept Pros and Cons
Youngjoong,
K., Jungyun. S
Automatic
text
categorizatio
n by
unsupervised
learning.
The goal of text categorization is to classify
documents into a certain number of predefined
categories. The previous works in this area have
used a large number of labeled training documents
for supervised learning. One problem is that it is
difficult to create the labeled training documents.
While it is easy to collect the unlabeled documents, it
is not so easy to manually categorize them for
creating training documents. In this paper, we
propose an unsupervised learning method to
overcome these difficulties. The proposed method
divides the documents into sentences, and
categorizes each sentence using keyword lists of
each category and sentence similarity measure. And
then, it uses the categorized sentences for training.
The proposed method shows a similar degree of
performance, compared with the traditional

15. This is a PHP Project entitled Comment Polarity Movie Rating System. This
is a webbased application that calculates the success rating or review
ratings of a movie. The
project uses a simple sentiment-based analysis to identify or calculate
the movie success
rate. This has a simple user interface and easy-to-use. The application
contains userfriendly functionalities and features. Movie rating system
where users are allowed to rate
and comment on movies online. These ratings are provided as input to
the website admin.
The admin then checks reviews, critic‟s ratings and displays an online
rating for every
movie. The purpose of this project is to develop an online system that
automatically allows
users to post reviews and store them. The system will analyze this data
(comments) to
SUMMARY OF LITERATURE SURVEY
CONCEPT DESCRIPTION :

17. ARCHITECTURE DIAGRAM

18. LIST OF MODULES
LOGIN MODULE
REGISTER MODULE
USER MODULE

19. LOGIN MODULE

21. REGISTER MODULE

23. USER MODULE

25. [1]. Kudakwashe Zvarevashe, Oludayo O Olugbara, “A Framework for Sentiment
Analysis with Opinion Mining of Hotel Reviews”, Conference on Information
Communications Technology and Society (ICTAS), 2018, IEEE.
[2]. Asiri Wijesinghe, “Sentiment Analysis on Movie Reviews”, Australian National
University Technical Report – RESEARCH GATE, October 2015.
[3]. Zainab Mirza, Mehwash Khan, Saima Khan, Khurshid Khatri, “Movie Rating
System Based On Opinion Mining”, International Journal of Interdisciplinary Research
and Innovations, Vol. 3, Issue 2, pp: (34-40), Month: April - June 2015.
[4]. Sagar Chavan, Akash Morwal,Shivam Patanwala,Prachi Janrao, “Sentiment
Analysis of Movie Rating System”, IOSR Journal of Computer Engineering (IOSRJCE), eISSN: 2278-0661, p- ISSN: 2278-8
[5]. Ramez Elmasri and Shamkant B. Navathe, “Fundamentals of Database Systems”,
7th Edition, 2017.
[6]. Ramakrishnan and Gehrke, “Database Management Systems”, 3rd Edition, 2014.
[7]. Salvetti, F., Reichenbach, C., Lewis, S.: Automatic opinion polarity classification of
movie review. Colorado Res. Linguist. 17, 420–428 (2004)
[8]. Beineke, P., Hastie, T., Vaithyanathan, S.: The sentimental factor: Improving review
classification via human-provided information. In: Proceedings of the 42nd Annual
Meeting on Association for Computational Linguistics (2004)
[9]. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods
for sentiment analysis. Comput. Linguist. 37, 267–307 (2011)
[10]. Lin, Y., Zhang, J., Wang, X., Zhou, A.: An information theoretic approach to
sentiment polarity
classification. In: Proceedings of the 2nd Joint
WICOW/AIRWebWorkshop on Web Quality (2012)
REFERENCES