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.
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