Rating Bollywood Movies on the basis of Online Reviews

Harsh Ghuriani and Naveen Sharma

Introduction

Movie is one of the most important means of entertainment in the world. Most often people want to know the opinions of the movie in advance either by word of mouth or by looking through the movie review sites. Innumerable user generated information is on the Internet as tweets, blogs, review, comments and likes. There are millions of reviews available and these reviews need to be explored, analyzed and organized for a better decision making. There is a great need of summarizing the movie review information available in different web sites. Opinion mining is a natural language processing that deals with the knowledge extraction of the opinions from the review texts.

What and how people think has always been an important piece of information for most of us during the decision making process. Opinion matters and affects all of us. Web is the source of many research works and Opinion Mining is one such area of research work. Internet and Web made it possible to find opinions and experiences of people who are neither our personal acquaintances nor well known professional critics.

The cheap access of internet to millions of users and the technology focused generations has contributed to generation of large amounts of data, which are mainly in the form of unstructured data. Most of the times the actual reviews are so long that it is impossible to read even a few of them and bring out conclusions from them. In these cases, customers may eventually tend to reading a few reviews in order to form a decision regarding the movie and gets a one sided view of the movie.

Literature Survey

S. No. Title Authors Year Technology Used Conclusion

1. Mining Online Reviews for Predicting Sales Performance: A

Xiaohui Yu, Yang Liu, Xiangji Huang, Aijun

2012 Sentiment PLSA,ARSA(Autoregressive Sentiment-Aware), ARSQA(Autoregressive

i)Both the sentiments expressed in the reviews

Case Study in the Movie Domain

An Sentiment and Quality Aware)

and the quality of the reviews have a significant impact on the future sales performance

ii) Extensive experiments conducted on a large movie data set confirm the effectiveness of the proposed approach.

2. Do Online Reviews Reflect a Product's True Perceived Quality? - An Investigation of Online Movie Reviews Across Cultures

Noi Sian Koh; Nan Hu; Clemons, E.K.

2010 Behavioral theory to capture intentions in rating online movie reviews.

Under-reporting bias occurs when consumers with extreme opinions are more likely to report their opinions than consumers with moderate reviews, resulting in reviews that may be biased estimators of quality and certainly have higher variance.

3. Analysing user ratings for classifying online movie data using various

Jyoti; Dhawan, S.; Singh, K.

2015 The data is classified into five different classes namely: bad, ok, average, good and excellent

An attempt has been made to analyze the best classifier for movie data based on

classifiers to generate recommendation

users' ratings and then the classification is used for making the recommendations for users.

4. Predicting movie sales revenue using online reviews

Rui Yao, Jianhua Chen

2013 Sentiment analysis and machine learning methods

Experiments indicate that the autoregressive model using both review sentiment data and the previous days' sale data results in higher accuracy than just using previous sale data alone.

5. A Survey on Sentiment Classification of Movie Reviews

Jyotika Yadav

2014 i) Document based SentiWordNet Approach

ii) Markov model approach.

In this research we propose way to automatically classify movie reviews in terms of positive, negative and neutral classes using hidden markov model approach.

6. Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level

V.K. Singh, R. Piryani, A. Uddin, P. Waila

2013 An aspect oriented scheme that analyses the textual reviews of a movie and assign it a sentiment label on each aspect.

The results obtained show that our scheme produces a more accurate

sentiment classification

and focused sentiment profile than the simple document-level sentiment analysis.

Factors affecting the Rating of Bollywood Movies (based on Online Reviews)

1) How many reviews are posted for the movie? How many of them are positive and negative?

2) What is the impact of the number of reviews on movie rating?

3) What are the different online review sources for the movie?

4) How does a positive and a negative review effect the rating of the Bollywood movie?

5) How reliable is the review from a particular online source considered?

6) Are the fake online reviews identified? If yes, do they count?

7) Does the information about the reviewer effects the reliability of the review?

8) Does the time of posting the review matter for rating a movie?

9) In Bollywood, people also post positive reviews just because of their favorite actor/actress in the movie; does it have an impact on the rating?

10) Whose reviews are more trusted: A person who has been posting reviews from quite a long time; or a newbie whose reviews get more likes?

11) How do the reviews have different impacts on rating of a new movie and rating of an older movie?

References

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