sentiment analysis using python and machine learning

1. Sentiment Analysis
using Python and ML
Abhinav Sachan(1616410005)
Prakhar Srivastava(1616410178)
Pravin Singh Katiyar(1616410194)
Pranveer Singh Institute Of
Technology

2. WHAT IS MACHINE LEARNING ?
 Machine learning is an application of artificial intelligence (AI) that provides
systems the ability to automatically learn and improve from experience without
being explicitly programmed.

3. SUPERVISED LEARNING:
Supervised learning as the name indicates a presence of supervisor as teacher.
Basically supervised learning is a learning in which we teach or train the machine
using data which is well labeled that means some data is already tagged with
correct answer. After that, machine is provided with new set of examples(data) so
that supervised learning algorithm analyses the training data(set of training
examples) and produces an correct outcome from labeled data.

4. UN-SUPERVISED LEARNING:
 Unsupervised learning is the training of machine using information that is
neither classified nor labeled and allowing the algorithm to act on that
information without guidance. Here the task of machine is to group unsorted
information according to similarities, patterns and differences without any prior
training of data.
 Unlike supervised learning, no teacher is provided that means no training will
be given to the machine. Therefore machine is restricted to find the hidden
structure in unlabeled data by our-self.

5. WHAT IS SENTIMENT ANALYSIS OR
OPINION MINING ?
 In simple words…
“The practice of applying
Natural Language Processing and Text Analysis
Technique to identify
And extract
Subjective information from a piece of text”

6. WHAT IS SENTIMENT ANALYSIS?
 It is classification of the polarity of given text in a document ,sentence or
phrase.
 The goal is to determine whether the expressed opinion in the text is
positive , negative or neutral.
 Enable us to track attitudes and feeling on web based on blog posts,
comments , reviews and tweets on differ topics
 Gives insight into the emotions behind the words.

7. WHAT IS SENTIMENT ANALYSIS?

8. MOTIVATION
 An aspect of social media data such as Twitter, face book messages and IMDb
, amazon is that it includes rich structured information about the individuals
involved in the communication.
 Online sites can be a genuine source for collecting opinions.
 It can lead to more accurate tools for extracting semantic information.
 It provides means for empirically studying properties of social interactions.

9. TARGETED WORDS :

10. PROBLEM STATEMENT
 The problem in sentiment analysis is classifying the polarity of a given text at the
document, sentence, or feature/aspect level .
 Whether the expressed opinion in a document, a sentence or an entity
feature/aspect is positive, negative, or neutral .
 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.

11. OVERVIEW OF WORKING OF
SENTIMENT ANALYSIS

12. WORKING:
Data Collection :
Public sentiments from consumers expressed on public
forums and on social network are collected Opinions and
feelings are expressed in different way, with different
vocabulary, context of writing, usage of short forms and slang,
makes data huge and disorganized.

13. WORKING:
Analyze Data:
Text Preparation Data is extracted and filtered before analysis
Non-textual content and content is identified and eliminated if
it is irrelevant Sentiment Detection .
Ex: keywords like “a,an,the,or etc” are eliminated.
Each sentence and opinion is examined for subjectivity
Sentences with subjective expressions are retained and ones
that convey objective expressions are discarded.

14. WORKING:
Indexing:
Sentiments can be broadly classified into two groups, positive
and negative Each subjective sentence is classified into
positive, negative, good, bad, like, dislike

15. WORKING:
Delivery :
(Presentation of Output) The result of converted unstructured
text into meaningful information Usually displayed as graphs
for easy interpretation.

16. PROPOSED METHODOLOGY:

17. CHALLENGES:

18. WHY TWITTER?
 Data: Twitter gives plenty of data. For analysis, it's almost like picking a needle
from a haystack. Finding relevant tweets, removing noise, takes a little extra
effort.
 Informal Language: Tweets don't follow the conventional grammatical structure.
We've got cases like:
 "OMG that's soooooo cool"
 "His moves are sick, man."
 "The iPhone's perfect for my insta."
 "he's on stage, i;m crYING halp !!11!!“
Opinions: Twitter opinions are sensitive to the user. They're also dependent on
author authority- a 14 year old girl's opinion on a novel may not hold the same
weight-age as that of another writer's.

19. ALGORITHMS TO BE IMPLEMENTED
1. Naive Bayes Classifier : Naive Bayes Classifier uses far less computing power
compared to other methods and often is a baseline method for many models.
2. Maximum Entropy Classifier: Maximum Entropy Classifier is a parameterized
method and works by extracting features from the text and combining the features
in a linear fashion for classification. This is a member of the log-linear or
exponential family of classifiers.
3. Decision Tree: Decision Tree works by creating a decision tree of root,
branches and leaves, creating a decision point at every branch. The decision is
taken at the leaf node.

20. TECHNOLOGY TO BE USED
 Concepts of Data Mining and Information and Information Retrieval.
 Python Language
 Twitter data set for training set
 Tweepy: Tweepy, the Python client for the official Twitter that supports
accessing Twitter via Basic Authentication and the newer method, OAuth.
Twitter has stopped accepting Basic Authentication.
 TextBlob: TextBlob, one of the popular Python libraries for processing textual
data, stands on the NLTK . TextBlob has some advanced features like –
Sentiment Extraction
Spelling Correction
 NLTK (Natural Language Toolkit)

21. USP(UNIQUE SELLING PROPORTION)
OF WORK
 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.
 Through comprehensive analysis, businesses gain valuable insights towards
their customers

22. HOW IT CAN BE HELPFUL ?

23. ROLE OF INDIVIDUAL MEMBERS
 Prakhar Srivastava:
Development and Model Training.
 Abhinav Sachan:
Development and Collecting Dataset.
 Pravin Singh Katiyar:
Development and Testing.

24. THANK
YOU