This document presents a system for predicting movie success and Academy Award wins using sentiment analysis and social network analysis. It discusses objectives like literature review and the model, system architecture, features and applications, limitations, and future scope. The system architecture has three tiers - presentation, server, and data. Multiple linear regression is used as the model. Features include forecasting movie success rates, revenues, and sentiment analysis of tweets. Limitations include accuracy improving over time and issues with Twitter data.

1. Welcome To
our
Presentation

2. Topic
Predicting Movie Success and
Academy Awards through
Sentiment and Social Network
Analysis

3. Submitted By-
Submitted To-
Samia Nawshin
Lecturer
Dept. Of CSE
Daffodil International University

4. Objectives
 Literature survey
 Model employed
 System Architecture
 Features & Applications
 Problem statement
 Limitation
 Salient Features
 Future Scope

5. Literature Survey
 Current Scenario of movie industry
 Forecasting Methods Employed
 Social Media
 Sentiment Analysis

6. System Architecture
 Presentation Tier
 The top most level of application is the user
interface
 The main task of this layer is to translate
tasks that user can understand .
 Server Tier
 The layer coordinates the application , processes
commands , makes logical decisions and performs calculations.
It also moves and processes data between two layers.
 Data Tier
 Here information is stored and retrieved from a database or
file system. The information is then passed back to the logical

7. Model employed
 Multiple Linear regression
 The regression coefficients are calculated using partial differentiation and by using the
particular data set available

8. Features & Applications
 Forecast movie success rate
 Estimate revenue from movie
 Compare movies
 Hype analysis
 Effect on public holyday on success
 Hyphens of tweets
 Twitter affinity-which tweets are interrelated

9. Problem Statement
 To demonstrate how social media content can be used to predict real-world outcomes.
In particular, we use the chatter from Twitter.com to forecast box-office revenues for
movies.
 We further demonstrate how sentiments extracted from Twitter can be further utilized to
improve the forecasting power of social media.

10. Limitation
 Forecasting Accuracy improves over time
 Twitter limitations
-It’s considered a news network
-Due to Twitter API limitations only 1% of tweets can be caught
-Only tweets in English language accepted
 Data cleaning limitations
– Presence of reference to two or more movies
– Presence of sarcastic tweets
– Emoticons

11. Salient Features
 Client-server architecture
• Accurate prediction
• Displays
– Sentiment of tweets
– tag cloud of tweets
– Location of tweet
– Rate of tweets per hour

12. Future Scope
 Tweets in other language can be taken into account with translators
 Sentiments from Facebook and other site can be added

13. Thank You