Sentiment analysis is a technique used to determine whether a text has a positive, negative, or neutral opinion. It can help businesses understand customer sentiment from reviews and feedback. There are different types of sentiment analysis, including detecting specific emotions, grading sentiments on a scale, and analyzing the sentiment towards particular aspects of a topic. Sentiment analysis is important because it allows automated large-scale analysis of unstructured data to identify customer needs and issues in real-time.

1. Sentiment Analysis

2. Positive or negative movie review?
• unbelievably disappointing
• Full of crazy characters and richly applied satire, and
some great plot twists
• this is the greatest screwball comedy ever filmed
• It was pathetic. The worst part about it was the boxing
scenes

3. What is SA & OM?
Identify the orientation of opinion in a
piece of text
Can be generalized to a wider set of
emotions
The movie
was fabulous!
The movie
stars Mr. X
The movie
was horrible!

4. Google Product Search
• a

5. Bing Shopping
• a
4

6. Sentiment analysis has many other names
• Opinion extraction
• Opinion mining
• Sentiment mining
• Subjectivity analysis

7. Why sentiment analysis?
• Movie: is this review positive or negative?
• Products: what do people think about the new iPhone?
• Public sentiment: how is consumer confidence? Is despair
increasing?
• Politics: what do people think about this candidate or issue?
• Prediction: predict election outcomes or market trends
from sentiment

8. Scherer Typology of Affective States
• Emotion:brief organically synchronized … evaluation of a major event
• angry, sad, joyful, fearful, ashamed, proud, elated
• Mood: diffuse non-‐caused low-‐intensitylong-‐durationchange in subjective feeling
• cheerful, gloomy, irritable, listless, depressed, buoyant
• Interpersonalstances: affective stance toward another person in a specific
interaction
• friendly, flirtatious, distant, cold, warm, supportive, contemptuous
• Attitudes: enduring, affectively colored beliefs, dispositions towards objects or
persons
• liking, loving, hating, valuing, desiring
• Personalitytraits: stable personality dispositions and typical behavior tendencies
• nervous, anxious, reckless, morose, hostile, jealous

9. Sentiment Analysis
• Sentiment analysis is the detection of attitudes
“enduring, affectively colored beliefs, dispositions towards objects or persons”
1. Holder (source) of attitude
2. Target (aspect) of attitude
3. Type of attitude
• From a set of types
• Like, love, hate, value, desire, etc.
• Or (more commonly) simple weighted polarity:
• positive, negative,neutral, together with strength
4. Text containing the attitude
• Sentence or entire document

10. Sentiment Analysis
• Simplest task:
• Is the attitude of this text positive or negative?
• More complex:
• Rank the attitude of this text from 1 to 5
• Advanced:
• Detect the target, source, or complex attitude types

11. Definition of Sentiment Analysis
• Sentiment analysis (or opinion mining) is a natural
language processing (NLP) technique used to
determine whether data is positive, negative or
neutral.
• Sentiment analysis is often performed on textual data
to help businesses monitor brand and product
sentiment in customer feedback, and understand
customer needs.

12. Tripod of Sentiment Analysis
Cognitive
Science
Natural
Language
Processing
Machine
Learning
Sentiment
Analysis
Natural
Language
Processing
Machine
Learning

13. Challenges
• Contrasts with standard text-
based categorization
• Domain dependent
• Sarcasm
• Dissatisfied expressions
Mere presence of words is
Indicative of the category
in case of text categorization.
Not the case with
sentiment analysis
• Contrasts with standard text-
based categorization
• Domain dependent
• Sarcasm
• Dissatisfied expressions
Sentiment of a word
is w.r.t. the
domain.
Example: ‘unpredictable’
For steering of a car,
For movie review,
• Contrasts with standard text-
based categorization
• Domain dependent
• Sarcasm
• Dissatisfied expressions
Sarcasm uses words of
a polarity to represent
another polarity.
Example: The perfume is so
amazing that I suggest you wear it
with your windows shut
• Contrasts with standard text-
based categorization
• Domain dependent
• Sarcasm
• Dissatisfied expressions
the sentences/words that
contradict the overall sentiment
of the set are in majority
Example: The actors are good,
the music is brilliant and appealing.
Yet, the movie fails to strike a chord.

14. Types of Sentiment Analysis
• Sentiment analysis focuses on the polarity of a text (positive, negative,
neutral) but it also goes beyond polarity to detect specific feelings and
emotions (angry, happy, sad, etc), urgency (urgent, not urgent) and
even intentions (interested v. not interested).
• Some of the most popular types of sentiment analysis:
1. Graded Sentiment Analysis
2. Emotion detection sentiment analysis
3. Aspect-based Sentiment Analysis
4. Multilingual sentiment analysis

15. 1. Graded Sentiment Analysis
• If polarity precision is important to your business, you might consider
expanding your polarity categories to include different levels of positive and
negative:
 Very positive
 Positive
 Neutral
 Negative
 Very negative
• This is usually referred to as graded or fine-grained sentiment analysis, and
could be used to interpret 5-star ratings in a review, for example:
Very Positive = 5 stars
Very Negative = 1 star

16. 2. Emotion detection sentiment analysis
• Emotion detection sentiment analysis allows you to go beyond polarity to
detect emotions, like happiness, frustration, anger, and sadness.
• Many emotion detection systems use lexicons (i.e. lists of words and the
emotions they convey) or complex machine learning algorithms.
• One of the downsides of using lexicons is that people express emotions in
different ways. Some words that typically express anger, like bad or kill (e.g.
your product is so bad or your customer support is killing me) might also
express happiness (e.g. you are killing it).

17. 3. Aspect-based Sentiment Analysis
• Usually, when analyzing sentiments of texts you’ll want to know which
particular aspects or features people are mentioning in a positive, neutral, or
negative way.
• That's where aspect-based sentiment analysis can help, for example in a
product review: "The battery life of this camera is too short", an aspect-
based classifier would be able to determine that the sentence expresses a
negative opinion about the battery life of the product in question.

18. 4. Multilingual sentiment analysis
• Multilingual sentiment analysis can be difficult.
• It involves a lot of preprocessing and resources. Most of these resources are
available online (e.g. sentiment lexicons), while others need to be created
(e.g. translated corpora or noise detection algorithms), but you’ll need to
know how to code to use them.
• Alternatively, you could detect language in texts automatically with a
language classifier, then train a custom sentiment analysis model to classify
texts in the language of your choice.

19. Why Is Sentiment Analysis Important?
Since humans express their thoughts and feelings more openly than ever before, sentiment analysis is fast becoming an essential tool to monitor and understand
sentiment in all types of data.
Automatically analyzing customer feedback, such as opinions in survey responses and social media conversations, allows brands to learn what makes customers
happy or frustrated, so that they can tailor products and services to meet their customers’ needs.
For example, using sentiment analysis to automatically analyze 4,000+ open-ended responses in your customer satisfaction surveys could help you discover why
customers are happy or unhappy at each stage of the customer journey.
Maybe you want to track brand sentiment so you can detect disgruntled customers immediately and respond as soon as possible. Maybe you want to compare
sentiment from one quarter to the next to see if you need to take action. Then you could dig deeper into your qualitative data to see why sentiment is falling or
rising.

20.  Sorting Data at Scale
Can you imagine manually sorting through thousands of tweets, customer support conversations, or surveys? There’s
manually. Sentiment analysis helps businesses process huge amounts of unstructured data in an efficient and cost-
 Real-Time Analysis
Sentiment analysis can identify critical issues in real-time, for example is a PR crisis on social media escalating? Is an
analysis models can help you immediately identify these kinds of situations, so you can take action right away.
 Consistent criteria
It’s estimated that people only agree around 60-65% of the time when determining the sentiment of a particular text.
subjective, influenced by personal experiences, thoughts, and beliefs.
By using a centralized sentiment analysis system, companies can apply the same criteria to all of their data, helping
insights.
The applications of sentiment analysis are endless. So, to help you understand how sentiment analysis could benefit
examples of texts that you could analyze using sentiment analysis.
Then, we’ll jump into a real-world example of how Chewy, a pet supplies company, was able to gain a much more
reviews through the application of sentiment analysis.

21. The Best Sentiment Analysis Tools
1.MonkeyLearn
2.Lexalytics(लेक्सलललिक्स)
3.Brandwatch
4.Social Searcher
5.MeaningCloud
6.Talkwalker's Quick Search
7.Rosette (रोसेि)
8.Repustate
9.Clarabridge
10.Aylien
11.Social Mention
12.Critical Mention
13.Awario
14.Hootsuite Insights
15.Hubspot's Service Hub

22. Sentiment Analysis Examples
To understand the goal and challenges of sentiment analysis, here are
some examples:
Basic examples of sentiment analysis data
 Netflix has the best selection of films
 Hulu has a great UI
 I dislike like the new crime series
 I hate waiting for the next series to come out