The document discusses a web application for sentiment analysis on social networking sites, allowing users to express opinions and receive ratings on posts. It outlines a proposed system utilizing a lexicon-based sentiment analysis algorithm for real-time opinion mining and classification of sentiment scores using machine learning. The application is beneficial for users seeking reviews and functions as a platform for discussions on various topics.

1. SENTIMENT ANALYSIS ON SOCIAL NETWORKING SITES

2. CONTENTS:
► Abstract
► Introduction
► Existing System
► Proposed System
► Modules
► Conclusion
► References

3. ABSTRACT:
► Sentiment Analysis on social networking sites is a web Application.
► In this user will post his views related to some subject or event or product, other users will view this post and
will comment on this post.
► Comments of various users, based on opinion, System will specify whether the posted topic is good or bad.
► System will use database and will rank the topic
► The role of the admin is to add post and adds keywords in database

4. INTRODUCTION:
► Sentiment Analysis is the Process of computationally identifying and categorizing
opinions from piece of text, and determine whether the writer’s attitude towards a
particular topic/product/event is positive or negative or neutral.
► Classifying the polarity of a given text as positive or negative is the basic task of
sentiment analysis.
► Sentiment analysis is used in different domains such as entertainment, education,
shopping etc…
► Sentiment analysis is often referred to with different names such as Opinion Mining,
Sentient classification, Sentiment analysis, and Sentiment extraction.

5. EXISTING SYSTEM:
► Information propagates through social networks, which is typically supported by the
three features of microblogs: short and simple contents.
► More and more users of microblogs tend to share small and big deals express opinions
and sentiments on current issues and discuss various topics.
► Therefore, the huge amount of tweets provides us with rich information about real
world events, in which opinions and sentiments are essential.

6. DISADVANTAGES:
▪ Problem with calculation of tweets or comments score of the tweets or comment the
given post.
▪ Retweeting the comments through the charts and telling hot opinions about topic
▪ Analysing ,calculating the score of tweets.

7. PROPOSED SYSTEM:
► We design and implement real-time prototype to perform opinion mining for tweets.
► Identifies opinion words using syntactic relations, and classifies sentiment orientation of
the sentence with lexicon-based method and summarizes all the opinion triples.
► Lexicon-based sentiment analysis algorithm is proposed to calculate the sentiment score
of tweets effectively
► Comments of various users, based on opinion, System will specify whether the posted
topic is good or bad
► Implementation is done using SVM supervised machine learning algorithm by creating
hyper planes

8. Advantages:
► Automatically give rating to the comments whether the given post is Good or Bad.
► To Overcome the calculating score of tweets or comments.
► Lexicon-based algorithm is easy to understand.

9. Software Requirements:
► Front End : Python, HTML
► Back End : MySql

10. Hardware Requirements:
► Processor : i3
► Hard Disk : 5GB
► Memory : 1GB RAM

11. Modules:
Admin Module:
▪ Add post
▪ Add Keywords
User Module:
▪ User signup
▪ Edit profile
Comment Module:
▪ Comment
▪ View comment
▪ Rating calculation
Status:

12. Conclusion:
► This system is useful for the users who need review about their new idea and also useful
for the users who need review about any particular event that is posted.
► This application also works as an advertisement which makes many people aware about
the topic posted.

13. Reference:
► “Sentiment analysis and opinion mining”, synthesis Lectures on Human Language
Technologies.
► “Mining and summarizing customer reviews”, in proceedings of the Tenth ACM
SIGKDD International Conference on knowledge Discovery and Data Mining.

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