Design Review.pptx

SENTIMENT ANALYZER USING MACHINE LEARNING PRESENTED BY: K.NAGA LOHITHA (15071A05K3) P.VARUN KUMAR (15071A05L5)

S.YESHWANTH

(15071A05M5)

E.SNIGDHA

(15071A05N8)

PROJECT GUIDE: DR. A.BRAHMANANDA REDDY

ASSOCIATE PROFESSOR/CSE

AGENDA • ABSTRACT

• USE-CASE DIAGRAM

• INTRODUCTION

• ACTIVITY DIAGRAM

• PROBLEM DEFINITION • COMPARATIVE STUDY • SYSTEM ARCHITECTURE • DESIGN METHODOLOGY

• CLASS DIAGRAM • SEQUENCE DIAGRAM • PLAN OF ACTION

• CONCLUSION • REFERENCES

ABSTRACT Sentiment analysis is the process of determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions and emotions expressed within an online mention. It is a contextual mining of text which identifies and extracts subjective information in source material. Depending upon the analysis the text is classified as positive, negative or neutral. Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics.

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 is positive, negative or neutral. We can determine the attitude by mining the text in a sentence or a document.

PROBLEM DEFINITION To build a machine learning model such that it analyses the emotion of a person by processing their tweets in Twitter. The emotion could be strongly positive, weakly positive, neutral, weakly negative or strongly negative.

COMPARATIVE STUDY EXISTING SYSTEM As the tweets are processed, the output obtained will be specifying the positivity, negativity and neutrality of the tweet that has been processed through the algorithm.

PROPOSED SYSTEM The result obtained would be classified as neutral, strongly positive ,positive ,weakly positive and same is the case with negative. The entire output is also represented in the form of a “pie chart” which depicts the percentage of each output.

SYSTEM ARCHITECTURE

DESIGN METHODOLOGY This project has been divided into 2 phases. • First, literature study is conducted, followed by system development. Literature study involves conducting studies on various sentiment analysis techniques and method that currently in use. • In phase 2, application requirements and functionalities are defined prior to its development. Also, architecture and interface design of the program and how it will interact are also identified. In developing the twitter sentiment analysis application, several tools are utilized, such as python shell 2.7.2 and notepad.

USE-CASE DIAGRAM System Register

Login

Input/Keywords

TweetsRetrivel

PreProcessing User

SentimentDetection

Analysis of Output

Logout

ACTIVITY DIAGRAM Register entry/register with details User Login entry/login with details

Input/Keywords do/Enter the user name of tweets

TweetsRetrivel do/Enetr the value for getting number of tweets

PreProcessing do/Remove the stopwords stemmind words

SentimentDetection do/get the polarity of given sentence

AnalysisOfOutput do/Display the results as positive,negitive,nutral

Logout exit/user logout

CLASS DIAGRAM Input/Keywords Register

TweetsRetrivel

Login

SentimentDetection

User AnalysisOfOutput

PreProcessing

Logout

SEQUENCE DIAGRAM Register

Login

Input/Keywords

TweetsRetrivel

PreProcessing

SentimentDetection

: User 1 : register with detailes() 2 : login with details() 3 : Enter the user name of tweets()

4 : Enetr the value for getting number of tweets()

5 : Remove the stopwords stemmind words()

6 : get the polarity of given sentence()

7 : Display the results as positive,negitive,nutral() 8 : user logout()

AnalysisOfOutput

Logout

PLAN OF ACTION STATUS OF WORK: CODING PHASE TIMELINE FOR FURTHER SDLC ACTIVITIES:

• CODING

28-02-2019

• TESTING

05-03-2019

• IMPLEMENTATION

10-03-2019

• DOCUMENTATION

20-03-2019

• PAPER PUBLICATION

30-03-2019

CONCLUSION Sentiment analysis can be used in blogs, articles, product reviews, social-media websites where a third person narrates his/her views. The project aims at classifying the comments based on the type of tweet made. This project, when deployed in social media platforms helps to propose a methodology through which sentiments can be analyzed.

REFERENCES [1] Maxim Stankevich, Vadim Isakov, Dmitry Devyatkin And Ivansmirnov(2017).Feature Engineering For Depression Detection In Social Media

[2]https://www.newgenapps.com/blog/the-secret-way-of-measuring-customer-emotions-social-media-sentimentanalysis[

[3]https://marcobonzanini.com/2015/05/17/mining-twitter-data-with-python-part-6-sentiment-analysis-basics

[4] https://link.springer.com/chapter/10.1007/978-3-319-49586-6_59

THANK YOU