Sentiment Analysis on social networking sites.pptx.pdf
- 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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