This presentation discusses sentiment analysis of tweets using Python libraries and the Twitter API. It aims to analyze sentiment on a particular topic by gathering relevant tweet data, detecting sentiment as positive, negative, or neutral, and summarizing the overall sentiment. The key steps involve accessing tweets through the Twitter API, preprocessing text by removing noise and stop words, applying sentiment analysis classification, and visualizing results with matplotlib. The goal is to determine the attitude of masses on a subject as expressed through tweets.

1. Presentation on
Twitter sentiment analysis
Submitted By:
Sonu Kumar
Submitted To:
Ms. Parul Gahlot

2. Contents
Introduction
OBJECTIVES
Sentiment Analysis
Libraries Used
Twitter API
Data Streaming
 Pre-processing Steps
Problem Statement
 Conclusion

3. Introduction
 twitter.com is a popular microblogging website.
 Each tweet is 140 characters in length.
 Tweets are frequently used to express a tweetersemotion on a particular
subject.
 There are firms which poll twitter for analysingsentiment on a particular
topic.
 The challenge is to gather all such relevant data,detect and summarize the
overall sentiment on atopic.

4. OBJECTIVES
● To implement an algorithm for automaticclassification of text into
positive, negative or neutral.
●Sentiment Analysis to determine the attitude of themass is positive,
negative or neutral towards thesubject of interest.
●Graphical representation of the sentiment in form ofPie-Chart.

5. Sentiment Analysis
 Sentiment analysis is contextual mining of text which identifies and extracts
subjective information in source material, and helping a business to understand
the social sentiment of their brand, product or service while monitoring online
conversations.
 It is the most common text classification tool that analyses an incoming message
and tells whether the underlying sentiment is positive, negative or neutral.

6. Libraries Used
• RE(Regular expression)-A Regular Expressions (RegEx) is a special
sequence of characters that uses a search pattern to find a string or set of
strings.
• Pandas-It is used to analyze data. It has functions for analyzing, cleaning,
exploring, and manipulating data.
• NumPy-used for working with arrays. It also has functions for working in
domain of linear algebra, Fourier transform, and matrices.
• Seaborn- Seaborn is a Python data visualization library based on
matplotlib. It provides a high-level interface for drawing attractive and
informative statistical graphics.
• Matplotlib-It is a comprehensive library for creating static, animated,
and interactive visualizations in Python. Matplotlib makes easy things easy
and hard things possible.

7. Twitter API
 An application program interface (API) is a set of protocols and tools for
building software applications. Basically, an API specifies how software
components should interact. Additionally, APIs are used when
programming graphical user interface (GUI) components.
 The Twitter API is simply a set of URLs that take parameters. They URLs
let you access many features of Twitter, such as posting a tweet or finding
tweets that contain a word.
 Tweepy is open-sourced library, hosted on GitHub and enables Python
to communicate with Twitter platform and use its API.

8. Data Streaming
 Data streaming is the process of transferring a stream of data from one
place to another, to a sender and recipient or through some network
trajectory. Data streaming is applied in multiple ways with various
protocols and tools that help provide security, efficient delivery and
other data results.

9. Start
Pre-processing Steps
 In this step of the project , tweets are mined using Twitter
Streaming API . Initially ,it cleans the unstructured textual data into
structured textual data by removing punctuations and additional
symbols.
1. Filtering : In this step , the special words , user names in twitter are
removed.
2. Tokenization : is the act of breaking up a sequence of strings into
pieces such as words, keywords, phrases, symbols and other elements
called tokens.
3. Removal of Stop Words : Articles and other stop words are removed
in this step.

10. Start

11. Problem Statement Given a message, decide whether the message is
of positive, negative, or neutral sentiment. For messages conveying
both a positive and negative sentiment, whichever is the stronger
sentiment should be chosen
Problem Statement

12. We will obtain a classification of polarities (of sentiments into positive ,
negative or neutral) and prepare a plot of the same using python
module like matplotlib.
Conclusion

13. Snapshot

14. Snapshot

15. THANK YOU!