A review on sentiment analysis and emotion detection.pptx

Areview on sentiment analysis and
emotion detection from text
Adnan Nawaz
MSCS-II
FA21-RCS-002
Advanced Data Mining
1
Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection
from text. Social Network Analysis and Mining, 11(1), 1-19.

Table of contents
 Abstract
 Introduction
 Use of
Social Media
 Review of Techniques of S & E Analysis
 Levels of Sentimental Analysis
 Emotion Models
 Basic Steps in Sentiment / Emotion detection
 Overview on Dataset used
 Techniques for sentiment analysis and emotion
detection
 Challenges in sentiment analysis and emotion
detection
 Conclusion Advanced Data Mining
2

Abstract
 Social Networking platform use for communicating
feelings.
 Textual content, pictures, audio, and video to express
their feelings.
 Massive amount of data is generated.
 Rapidly processed data through sentimental analysis.
 SA recognizes polarity in text.
 Author has positive, negative or neutral toward an
item, Administration, location, individual etc.
 Individual’s precise emotional/mental state.
3

Topics
 Levels of sentiment analysis.
 Various emotion models, and
 The process of sentiment analysis and emotion
detection from text.
 Challenges during sentiment and emotion analysis.
4

Introduction
 Critical areas of NLP are Sentiment Analysis and
Emotion Recognition.
 SA means Data is positive, negative or neutral.
 ER means furious, cheerful, or depressed.
 Use of social Media to communicate their feelings,
arguments, opinion.
 Feedbacks and reviews on various product and
services.
 Rating and reviews to encourage vendors and service
provider.
 Transforms unstructured data into meaningful insights
for decision making
5

Use of
Social Media
 Broadcast information about product and collect client
feedback.
 Feedback is valuable not just for business marketers
for satisfaction.
 Sentimental analysis helps marketers in
understanding their customer's perspectives.
 The rise of social media has made it easier and faster.
6

Healthcare
Sector
 Social media have become essential sources of health-
related information.
 Health practitioners must use automated sentiment
and emotion analysis to save patient
7

Education
Sector
 Sentiment Analysis plays a critical role for both
student
 Enthusiasm, talent, and dedication decides teacher
efficiency.
 Timely feedback from students to improve teaching
approaches.
 Sentiment Analysis and emotion analysis of textual
feedback.
 Social Media use for advertising and marketing
purpose.
 Students and Guardians conduct online research about
institutes, courses.
 Sentiment and emotion analysis can help the student
to select the best institute or teacher
8

Techniques of S
& EAnalysis
 Three techniques for sentiment and emotion analysis:
1) Lexicon based,
2) Machine learning based, and
3) Deep learning based.
 Researcher face significant challenges, including:
1) Dealing with context,
2) Ridicule,
3) Statements conveying several emotions,
4) Spreading Web slang,
5) and lexical and syntactical ambiguity.
9

Sentimental
Analysis
 A process of obtaining meaningful information and
semantics from text using natural processing
techniques
 Big data is generated through Social media.
 Sentiment Analysis is use to analyze it effectively and
Efficiently.
 Not restricted to just positive or negative.
 It can be agreed or disagreed, good or bad.
 5-point scale: strongly disagree, disagree, neutral,
agree, or strongly agree
10

Example
 Scale of 1 to 5 was used for Reviews on European and
US destinations labeled.
 e.g 1 or 2 stars for negative polarity.
 Gräbner et al. (2012) built a domain-specific lexicon:
 Consists of tokens with their sentiment value.
 Customer reviews in tourism domain
 5-star ratings from terrible to excellent
11

Levels of
Sentimental
Analysis
 Sentiment analysis is possible at three levels:
 Sentence level,
 Broken down into sentence
 Document level, and
 Sentiment detected for entire document.
 To extract global sentiment.
 Contain redundant local patterns and lots of noise.
 Link between words and phrases
 Aspect level
 Opinion about a specific aspect or feature is determined.
 The speed of the processor is high, but this product is
overpriced.
 Here, speed and cost are two aspects.
12

Aspect level
sentiment
analysis
 Devi Sri Nandhini and Pradeep (2020) proposed an
algorithm to extract:
 Implicit aspects from documents based and
 By exploiting the relation between opinionated (adj)
words and explicit aspects(Noun).
 Ma et al. (2019) took care of two issues:
 Different polarities of various aspects in a single
sentence.
 Explicit position of context in an opinionated sentence.
 Built up a two-stage model based on LSTM
 Context words near to aspect are more relevant and
 Need greater attention than farther context words.
13

Stages
 At stage One:
 Model exploits multiple aspects in a sentence one by one
with a position attention mechanism.
 At the second state
 Identifies (aspect, sentence) pairs according to the
position of aspect and context around it and
 Calculates the polarity of each team simultaneously.
14

Emotion
Detection
 Process of identifying a person’s various feelings or
emotions.
 For example, joy, sadness, or fury.
 Physical activities such as heart rate, shivering of
hands, sweating, and voice pitch
 From text, Emotion detection is difficult
 New slang or terminologies being introduced e.g LOL
 Emotion detection is challenging
15

Emotion
Models
 Dimensional Emotion model:
 Represents emotions based on three parameters:
 Valence, Arousal, and Power
 Valence means polarity, and
 Arousal means how exciting a feeling is.
 e.g, delighted is more exciting than happy.
 Power signifies restriction over emotion.
16

Dimensionalmodelof
emotions
 These parameters decides
 Position of psychological states in 2-dimensional space
17

Emotion
models
 Categorical Emotion model:
 Emotions are defined discretely,
 such as anger, happiness, sadness, and fear.
 Categorized into four, six, or eight categories.
18

Models used
byAuthors
Authors Model Emotions Purpose
Batbattar &
Becker
Ekman’s
Model
Six
Sailunaz &
Alhajj
Ekman’s
Model
Six Tweets
Robert Ekman with
“Love” state
Seven Tweets
Ahmad Wheel of
Emotion Model
by Plutchik
Nine states Labeling
Hindi
Sentences
Laubert &
Parlamis
Shahver Three
22

Common states
in various
Models
23

Basic Steps in
Sentiment /
Emotion detection
24

Pre-processing
of text
 Social media platform's posts, audits, comments,
remarks, and criticisms are highly unstructured
 Data Cleaning is necessary
 Including tokenization, stop word removal, POS
tagging, etc.
25

Tokenization
 Tokenization:
 “this place is so beautiful” and
 Post-tokenization, it will become
 'this,’ "place," is, "so," beautiful.’
 Converting the text into standard form.
 Correcting the spelling of words, etc.
27

Removal of
Stop Words
 Stop words like "is," "at," "an," "the"
 Avoid unnecessary computations.
 Finding various aspects from a sentence.
 Noun or Noun phrase describe various aspect.
 While and emotions are conveyed by adjectives.
28

Stemming and
lemmatization
 Two crucial steps of preprocessing.
 In stemming:
 words are converted to their root form
 The terms "argued“ and "argue" become "argue.“
 Lemmatization:
 Turn a work into base word.
 the term "caught" is converted into "catch“.
 Removing numbers and Lemmatization enhanced accuracy.
 Removing punctuation did not affect accuracy.
29

Feature
extraction
 The process of converting or mapping the text or words
to real valued vectors is called word vectorization.
 Document is broken down into sentences and the
Words.
 The resulting matrix, each row represents a sentence
or document.
 while each feature column represents a word.
30

Feature
extraction
 Straightforward methods used is 'Bag of Words' (BOW).
 Fixed-length vector of the count is defined.
 Each entry corresponds to a word in a pre-defined dictionary
 Count of 0 if it is not present in the pre-defined dictionary,
otherwise >=1.
 Vector length is always equal to the words present in the
dictionary.
 Easy Implementation.
 Drawbacks:
 Sparse Matrix.
 Loses the order of words in the sentence, and
 Does not capture the meaning of a sentence
 To represent the text “Are you enjoying reading”
 I, Hope, you, are, enjoying, reading would be (0,0,1,1,1,1)
 Can be Improved:
 Pre-processing of text and
 By utilizing n-gram, TF-IDF. Advanced Data Mining
31

N-Gram
 Excellent option to resolve the order of words in
sentence vector representation.
 The value of n can be any natural number.
 “To teach is to touch a life forever” and n = 3 called
trigram.
 Will Generate, 'to teach is,' 'teach is to,' 'is to touch,' 'to
touch a,' 'touch a life,' 'a life forever.’
 Perform better than the BOW.
32

Term frequency-
inverse document
frequency
 Used for feature extraction.
 Represents text in matrix form.
 Ahuja et al. (2019) implemented six pre-processing techniques
and
 Compared two feature extraction techniques to identify the best
approach.
33

Techniques for
sentiment analysis
and emotion
detection
34

Lexicon based
approach
 This method maintains a word dictionary.
 Each positive and negative word is assigned a
sentiment value.
 Mean value is used to calculate the sentiment of the
entire sentence or document.
 Two Approaches:
1. Dictionary Approach:
 Words of some language
 less efficient.
 Multiple domains with a data-driven approach.
2. Corpus Based:
 Random sample of text in some language.
 domain-specific sentiment words.
 Poor generalization.
 excellent performance within a particular domain
35

Machine
Learning based
approach
 Dataset is divided into two parts:
 Training and testing purposes.
 Supervised Classification
 Naive Bayes, support vector machine (SVM), decision
trees, etc.
 Gamon (2004) applied a SVM:
 Accuracy upto 85.47%.
 Ye et al. (2009) worked with SVM, N-gram model, and
Naive Bayes:
 Sentiment and review on seven popular destinations of
Europe and the USA.
 Accuracy of up to 87.17%
36

Deep Learning
basedApproach
 These algorithms detect the sentiments from text
without doing feature engineering.
 Multiple deep learning algorithms:
 RNN, CNN
 Authors applied the model to review the data of
Cornell movie:
 More accurate as compared to SVM.
 Pasupa and Ayutthaya (2019) use CNN, LSTM, and Bi-
LSTM.
 children’s tale (Thai) dataset.
 with or without features:
 POS-tagging
 Thai2Vec(word embedding trained from Thai Wikipedia)
 Sentic (to understand the sentiment of the word).
 Best performance in CNN
37

Transfer Learning
Approach and
HybridApproach
 Part of machine learning.
 Model trained on large datasets.
 To resolve one problem can be applied to other related
issues.
 Re-using a pre-trained model on related domains as a
starting point
 Can save time and produce more efficient results.
 Zhang et al. (2012) proposed a novel instance learning
method:
 Modeling the distribution between different domains.
 classified the dataset:
 Amazon product reviews and
 Twitter dataset into positive and negative sentiments.
38

 Evaluation of Matrix
40

Challenges in
sentiment analysis
and emotion
detection
 Lot of data in the form of informal text.
 Spelling mistakes, new slang, and incorrect use of
grammar.
 Sometimes individuals do not express their emotions
clearly.
 E.g “Y have u been soooo late?”
41

Challenges in
sentiment analysis
and emotion
detection
42

Conclusion
 Review of the existing techniques for both E and S
detection is presented.
 Lexicon-based technique performs well in both.
 Dictionary-based approach is quite adaptable and
straightforward to apply.
 Corpus based method is built on rules.
 Machine and deep learning algorithms depends on
dataset size and Preprocessing.
 LSTM Model can cover long-term dependencies and
extract features very well.
 Various approaches depends on preprocessing and
feature extraction.
43

Any Question?
THANK YOU
44

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