1. TWEEZER
(Twitter Sentimental Analysis)
Major Project Presentation
Piyush Aggarwal Rachit Goel
9910103445 9910103566
Department of CSE/IT

2. 1. Problem Statement
2. Introduction
3. Data Collection
4. Data Pre-Processing
5. Classification of Tweets
6. Working
7. Future Scope
Content

3.  A major benefit of social media is that we can see the good
and bad things people say about the particular brand or
personality.
 The bigger your company gets difficult it becomes to keep
a handle on how everyone feels about your brand. For
large companies with thousands of daily mentions on social
media, news sites and blogs, it’s extremely difficult to do
this manually.
 To combat this problem, sentimental analysis software are
necessary. These soft wares can be used to evaluate the
people's sentiment about particular brand or personality.
Problem Statement

4.  TWEEZER = TWEEts + analyZER
 This product (Tweezer) introduce a novel approach for
automatically classifying the sentiment of Twitter
messages. These messages are classified as positive or
neutral or negative with respect to a query term or the
keyword entered by a user.
Introduction: What is Tweezer!!

5. 1. Data Streaming: For performing sentimental analysis
we need Twitter data consisting of tweets about a
particular keyword or query term. For collecting the
data and tweets we have used Twitter public API
available for general public for free. It is the part of
Data Collection.
#NOTE: Tweets are short messages, restricted to 140
characters in length. Due to the nature of this micro
blogging service (quick and short messages), people use
acronyms, make spelling mistakes, use emoticons and
other characters that express special meanings.
Data Collection

6. It is a process to remove the unwanted words from
tweets that does not amount to any sentiments.
1. Emotional Icons- 170 emoticons; identified emotional
icons and remove them.
2. URLs-does not signify any sentiment; replaced it with
a word |URL|
Data Pre Processing

7. 3. Stop words- words as “a‟, “is”, “the”; does not
indicate any sentiment
4. UserNames and HashTags- @ symbol before the
username and # for topic; both replaced with AT_USER.
Data Pre Processing(cont..)

8. 5. Repeated Letters- huuuungry, huuuuuuungry,
huuuuuuuuuungry into the token “huungry".
6. Slag Words- Non English words
Data Pre Processing(cont..)

9. Different Ways of Classifications-
 Binary Classification: It is a two way categorization
i.e. positive or negative.
 3-Tier: In this, Tweets are categorized as Positive,
Negative and Neutral.
 5-Tier: Tweets are bucketed in 5 Classes namely:
Extremely Positive, Positive, Neutral, Negative and
Extremely Neutral.
Characterization of Tweets

10. Sarcasm types related to twitter are as follows:
 Positive words with negative smiley.
 Negative words with positive smiley.
 Sarcasm related to facts which includes spoofs,
sarcastic recreation etc.
Sarcasm Detection

11. 1. Data Pre-Processing using more parameters to get
best sentiments
2. Updating Dictionary for new Synonym and
Antonyms of already existing words.
3. Web-Application can be converted to Mobile
Application
4. Context Sentimental Analysis may be implemented
in future for accuracy purposes.
Future Scope

12. Complete Model

13. Thank You