TWITTER SENTIMENT ANALYSIS USING DEMPSTER SHAFER …€¦ · issues in the classification of the sentiment [13]. To address this problem, ... lemmatization, spelling correction, stop

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International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 2, February 2020, pp. 163-185, Article ID: IJARET_11_02_017

Available online at http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=2

ISSN Print: 0976-6480 and ISSN Online: 0976-6499

© IAEME Publication Scopus Indexed

TWITTER SENTIMENT ANALYSIS USING

DEMPSTER SHAFER ALGORITHM BASED

FEATURE SELECTION AND ONE AGAINST

ALL MULTICLASS SVM CLASSIFIER

Nagamanjula R

Research Scholar, Mother Teresa Women‟s University,

Kodaikanal, Tamilnadu, India

Dr. A. Pethalakshmi

Principal, Govt. Arts College for Women,

Nilakottai, Tamilnadu, India

ABSTRACT

Rapid development of social media and internet technologies has conquered much

intention on sentiment analysis. Twitter is one of the social media used by numerous

users about some subject matter in the form of tweets. Twitter Sentiment Analysis

(TSA) is the way of finding sentiments and opinions in the tweets. Still, achieving high

accuracy in TSA is difficult due to characteristics of twitter data such as spelling

errors, abbreviation and special characters. Therefore, our main intention is to attain

high accuracy in TSA. To achieve this intention, we majorly concentrated on five

processes: Data cleaning, Preprocessing, Feature extraction, Feature Selection and

Classification. In the data cleaning stage, we perform four major processes: URL

removal, Username Removal, Punctuation Removal and Spell Correction. By

executing the data cleaning process, this work enhances the efficacy of TSA. To

increase the accuracy in TSA, we adopt preprocessing where tokenization, stop word

removal, lemmatization and stemming, acronyms expansion, slangs correction, split

attached word, and POS tagging. In order to improve the classification accuracy, we

execute the feature extraction process where eight features are extracted. A key

bottleneck in TSA is a huge amount of data which makes difficulties in the training of

ML during sentiment classification. To tackle this hurdle, we select the best features

from the extracted features using the Dempster Shafer algorithm. Sentiments are

classified using the One Against All-Multiclass Support Vector Machine (OA2-SVM)

algorithm. It classifies sentiments into five classes: Strongly Positive, Strongly

Negative, Positive, Negative and Neutral. We implement these processes using public

tweets collected from the open repository. The results obtained from the simulation

are auspicious in terms of upcoming metrics including, Accuracy, Precision, Recall,

F-Measure and Error Rate. From the comparison results, it perceived that our method

enhances 25% in Accuracy and Precision, 30% in Recall, 20% in F-Measure and

Nagamanjula R and Dr. A. Pethalakshmi


reduces 29% in error rate compared to the existing methods including LAN2FIS, GA

and HCS.

Keywords: Twitter Sentiment Analysis, Split Attached Word, acronyms expansion,

Preprocessing, Feature Selection, Classification

Cite this Article: Nagamanjula R and Dr. A. Pethalakshmi, Twitter Sentiment

Analysis using Dempster Shafer Algorithm Based Feature Selection and One against

all Multiclass SVM Classifier, International Journal of Advanced Research in

Engineering and Technology (IJARET), 11 (2), 2020, pp 163-185.

http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=2

1. INTRODUCTION

With the emergence of social media platforms has set web users a space to express and share

their thoughts, opinions on all categories of topics and events [1-3]. Twitter is one of the most

popular social media services around the world where users publish status update called

tweets which are at most 140 characters. Tweets are used to convey their followers about

what they are thinking, what they are exploiting and what they are happening around them in

their daily lives. It allows users to announce their interests, favorites, opinions and sentiments

towards various issues and matters. Hence, twitter has numerous amounts of data with

different sentiment polarities.

Sentiment analysis becomes a hot research area among researchers due to its wide range

of application [4]. The core objective of sentimental analysis on twitter data is to classify the

sentiment polarity of the tweets as positive, negative and neutral [5]. Twitter data comprises

of more slangs, acronyms, misspelled words and so on [6]. As a result, the performance of the

TSA drops drastically when employed to determine the sentiment of the tweets.

To tackle this issue, preprocessing is included before performing sentiment analysis

process [7]. Preprocessing performs tasks including tokenization, stop word removal, URL

removal and so on [8]. These processes enhance the performance of sentimental analysis on

twitter data [9]. There exist plenty of tweets in the twitter dataset, in order to analyze the

sentiment accurately perfect features must be extracted from the tweets [10].

The feature extraction process is included in TSA which sets objective as to extract

features from the tweets [11]. Features extracted in the TSA are unigram, bigram, trigram,

emotional, slang related features, etc. [12]. Features extracted from the tweets cause serious

issues in the classification of the sentiment [13]. To address this problem, feature selection

techniques are arisen such as chi square, modifier chi square methods are introduced [14].

Finally, classification algorithms such as naïve bayes, maximum entropy, and SVM are used

to classify the sentiment of the tweets [15].

Although numerous techniques exist on sentiment analysis of twitter data, attaining high

accuracy is still a big hurdle due to the following issues:

Tweeter data have lots of short segments of text, rich in abbreviation and slang, typos

or grammatical error, etc…These characteristics complex the sentiment analysis

process.

Lack of features for classifying the tweets that tend to reduce the classification

accuracy.

Difficulties in ML based sentiment analysis due to huge amounts of data.

The main objective of this paper is to enhance the accuracy in the sentimental analysis of

tweets. With the proposed system, we seek out to answer the following research questions

(Q):

Twitter Sentiment Analysis using Dempster Shafer Algorithm Based Feature Selection and One

against all Multiclass SVM Classifier


Q1. How our work achieves high accuracy in sentiment classification?

Q2. How we enhance the tweets in the twitter dataset in order to achieve the high

efficiency in the sentimental analysis?

Q3. Does our work extract optimal features to improve classification results?

Q4. How we reduce difficulties in ML based sentimental analysis?

1.1. Contribution

We summarize the major contributions of this work as follows:

Initially, we perform data cleaning in order to avoid discrepancy of twitter data where

URL removal, Username Removal, Punctuation Removal and Spell Correction are

performed.

Our work included the preprocessing step in TSA to increase the correctly classifies

instances. Preprocessing includes seven sequential processes: tokenization, stop word

removal, acronyms expansion, slang correction, split attached word, lemmatization

and stemming and POS tagging. In these acronyms expansion and split attached word

are new processes since twitter data comprises of acronyms and attached words.

In regard to achieve high accuracy in the TSA, we perform feature extraction in TSA.

In this eight sequential features are extracted that are, hashtag based features,

standardizing word features, slang related features, emotional features, semantic

features, syntactic feature, Negation features and unigram features.

To reduce hurdle in ML based classification, we carried out feature selection. In this,

feature selection is executed using the Dempster Shafer algorithm.

In order to increase the classification accuracy, we adopt OA2-SVM based

classification. It classifies the tweet into five classes that are strongly positive, strongly

negative, positive, negative and neutral.

1.2. Outline

This paper is structured as follows: Section 2 explains the state of art works with its

limitations. Section 3 demonstrates the problem statement of the works related to the

proposed TSA. Section 4 designates our proposed method briefly with proposed algorithms.

Section 5 claims the experimental results of our work with the comparison of existing

methods. At last, Section 6 concludes the contributions of this work and also provides some

comments on future direction.

2. LITERATURE REVIEW

This section reviews the works related to the twitter sentiment analysis associated with data

cleaning, preprocessing, feature extraction, feature selection and classification.

2.1. Preprocessing

Preprocessing is a substantial process in the TSA which is performed before going to into the

sentiment analysis processes. Muhammad et al. [16] have offered TSA with the hybrid

scheme. Here, preprocessing was performed before enter into the classification processes.

Preprocessing removes the URL, hashtag and username from the tweets. In order to, achieve

better performance in the classification step, here preprocessing was performed. Ghiassi et al.

[17] have suggested TSA using the supervised machine learning algorithm. In this,

preprocessing was executed in order to improve the efficiency of sentiment analysis processes



such as feature extraction and classification. Here, preprocessing adopts URL removal,

hashtag removal and username removal. Author discussed in papers [16-17] have ineffective

preprocessing in sentiment analysis since these methods don‟t concentrate on significant

preprocessing processes such as tokenization, Part of Speed (POS) tagging, etc.

Maria et al. [18] have suggested sentiment analysis with an aspect level approach. Here

preprocessing adopts six sequential processes that are normalization, tokenization, sentence

splitter, POS tagging, lemmatization and stop word removal. Normalization, tokenization and

stop words are performed in preprocessing in order to remove the redundant words from the

tweets. Muhammad et al [19] have introduced rule induction based sentiment analysis. In this,

preprocessing carried out succeeding processes that are tokenization, slang filter,

lemmatization, spelling correction, stop word removal and negation handling. Author

discussed in papers [18-19] have less accuracy in sentiment classification processes due to the

absence of stemming and split attached word.

Table 1 Limitations on Preprocessing Works

Author Main Focus Limitation

Muhammad et al. [16]

URL, hashtag and

username

Difficult sentiment

analysis process due to

the absence significant

steps in preprocessing

i.e. stop words removal,

POS tagging

Ghiassi et al. [17]

Maria et al. [18]

Normalization,

tokenization, sentence

splitter, POS tagging,

lemmatization and stop

word removal

Less Accuracy in

Sentiment Classification

due to ineffective

enhancement of tweets.

Muhammad et al. [19]

Tokenization, slang

filter, lemmatization,

spelling correction, stop

word removal and

negation handling

Table 1 illustrates the limitations of existing works related to the preprocessing in the TSA.

2.2. Sentimental Analysis without Feature Selection

Feature extraction is substantial in TSA in order to achieve accurate results in sentiment

analysis. Xuejun et al. [20] have pointed out subjectivity classification based TSA. Here,

multiple features are extracted before enter into the classification process. In this,

microblogging emotional features, semantic features and sentiment polarity features are

extracted and given as input to the classifier to analysis sentiment of the tweets. The

classification was performed using Convolutional Neural Network (CNN).

Ahmed et al. [21] have offered an enhanced approach for sentiment analysis in twitter. In

this, 40 features are extracted from the twitted dataset. Some of the features are listed as

follows: number of question marks, number of words per tweet, number of exclamation marks

and number of mentions and URLS. These features are directly given to the classification

process to analyze the sentiment of the tweets. CNN was used to classify the sentiment of

tweets. Author discussed in papers [20-21] has the following limitations: The absence of

feature selection induces difficulties in classification since it extracts some features from the

extraction step.

Ravinder et al. [22] have suggested sentiment analysis based on the feature extraction

techniques. Here, two different types of features are extracted. They are Term Frequency-




Inverse Document Frequency (TF-IDF) and N-gram features. In these, TF-IDF was used to

convert the textual representation into the Vector Space Model (VSM). N-gram features are

used to tokenize the tweets in twitter dataset. ML algorithms are used to classify the sentiment

of the tweets.

Samah et al. [23] have introduced sentiment analysis in twitter dataset. Here, features are

extracted from the twitter dataset in order to classify the sentiments. In these, two different

features are extracted that are texture and lexicon based features. In this, texture features are

extracted using Bag of Word features. Lexicon based features are used to classify the tweet

into a positive or negative sentiment. ML algorithms such as naïve bayes, SVM are used to

classify the sentiment of the tweets. More features are required to achieve high accuracy in

the classification of sentiment.

Muhammad et al. [24] have pointed out lexicon based classification for TSA.

Preprocessing was performed before classifying the text. Here, four types of classifiers are

used to classify the sentiment of the tweets. They are Emoticon Classifier (EC), Modifier and

Negation Classifier, Domain Specific Classifier and SentiWordNet (SWN) based classifier.

Complex classification processes since four classifiers are executed in a parallel manner.

Shufeng et al. [25] have suggested sentiment analysis in twitter using neural network

based classification. In this, N-gram features are extracted before enter to the sentiment

classification process. Here, a multilevel sentiment enriched word embedding model was used

to classify the sentiment into positive or negative. Here, tweets are classified into two

different classes that are positive and negative. It consumes more time to classify the

sentiment of each tweet.

Min et al. [26] have suggested aspect level sentiment classification for TSA. Hierarchical

Human like Aspect level Strategy (HHSS) was used to analyze the sentiment of the twitter

dataset. This model comprises of three components that are sentiment-aware mutual attention

module, an aspect-specific knowledge distillation module, and reinforcement learning based

re-reading module. Classification accuracy was very less due to a lack of features

consideration.

Jamilu et al. [27] have offered naïve bayes for classification of twitter sentiments. In this

features extracted before classifying the sentiment of the tweets. In this, two types of features

are extracted for sentimental analysis. They are unigram and n gram features. Here, naïve

bayes classifier was used to classify the positive and negative opinions of the tweets. In this,

naïve bayes classifier estimates sentiment score for each tweet in order to classify it into

positive or negative. Naïve bayes algorithm was not effective non-linear features since its

decision space is linear.

Aforesaid studies are associated with the sentimental analysis without feature selection. It

induces lots of issues in classifying twitter sentiments. Table 2 designates the limitations of

existing works related to sentimental analysis without feature selection in the TSA.

Table 2 Limitations on Sentimental Analysis without Feature Selection


Xuejun et al. [20] Extracting features from

tweets for classification

Complex feature

classification procedure. Ahmed et al. [21] Polarity feature

extraction and CNN

based classification

Ravinder et al. [22] VSM based feature

extraction and ML based

classification

More features are

required to achieve

high accuracy



Samah et al. [23] BoW feature extraction

and ML based

classification

Less accuracy in

classification

Muhammad et al. [24] Preprocessing the twitter

data and ensemble based

classification

Degrades performance of

the system

Shufeng et al. [25] N-gram based feature

extraction and sentiment

classification

Consumes more time

Min et al. [26] Neural network based

sentiment classification

Classification accuracy

is less

Jamilu et al. [27] N-gram based feature

extraction and ML based

classification

Not suitable for non-

linear features

2.3. Sentimental Analysis with Feature Selection

Feature selection processes are executed in the TSA approach to enhance the performance of

the classification. Jose et al. [28] have suggested feature selection based sentiment analysis. In

this, feature selection was performed after the completion of feature extraction. Feature

selection was executed with the aid of lingua. ML based classification algorithms are used to

detect the sentiment of the tweets. The complex feature selection reduces the accuracy of the

sentimental analysis of tweets.

Wu et al. [29] have pointed out a new approach to the analysis sentiment of the tweets.

Here, an interactive machine learning based approach was to find the sentiment of the tweet.

In this, interactive based feature selection was adopted where utility functions are computed.

Based on the computed utility functions, weight was assigned to each feature. From which

high weighted features are selected for classification. A proper feature selection mechanism

was required to achieve optimum results in classification.

Mohammed et al. [30] have offered an efficient TSA approach using feature selection and

ensemble classification. Extracted features are selected using the Information Gain (IG) based

feature selection technique. Information gain based feature selection techniques were used to

reduce the feature space. Reduced feature space is given as input to the ensemble classifier to

detect the sentiment of the tweets. The performance of the IG technique was degraded when

an increase in the size of the dataset.

Saputra et al. [31] have pointed out sentiment analysis using Pearson Correlation based

feature selection. In this, the Pearson correlation algorithm was used to diminish the feature

space and to obtain optimal features. The feature selection algorithm was further split into the

filter and wrapper types. Naïve bayes algorithm was used to select classify the sentiment of

tweets. Consumes more time in feature selection hence it cannot be used for large dataset.

Table 3 Limitations on Sentimental Analysis with Feature Selection


Jose et al. [28] Selecting features for

sentimental classification

Reduces classification

accuracy

Wu et al. [29] ML based features

selection and classification

Induces complexity in the

classification process

Mohammed et al. [30] IG based feature selection

and Classification

Not applicable to large scale

dataset

Saputra et al. [31] Pearson Coefficient based

feature selection and

Naïve bayes based

Classification

Consumes more time




Table 3 designates the limitations of existing works allied to the sentimental analysis with

feature selection in the TSA.

3. PROBLEM STATEMENT

This section illustrates the problem statement of existing works related to the proposed TSA.

Assume that, we consider collection of document (twitter data) in the training data. Each

document x in contain n tweets . Each tweet contains sentiment

words .

While processing the twitter document with sentiment words induces less accuracy

due to the subjective nature of tweets. We have identified problems in individual works that

are listed as follows:

Hybrid framework based sentiment analysis was performed by using genetic algorithm

based feature reduction [32]. In this, Genetic Algorithm (GA) was used to reduce feature size

which takes more time to find optimal solution and chromosome with the same fitness

function will not have the same probability of being selected. Hence, GA based feature

reduction become complex for the larger dataset. BoW doesn‟t able to predict semantic

relation between different words thus reduces the efficiency of feature extraction. Hybrid

Cuckoo Search (HCS) based sentiment analysis was performed on twitter data [33]. Here,

preprocessing was not effective, since it performs only a few operations that tend to reduce

accuracy. Feature extraction process extracts only emoji, exclamation and words with positive

and negative meaning that also reduces accuracy. Ensemble classification system based

sentimental analysis was performed on twitter data [34]. Diversity of base classifier was a

major problem in ensemble classifier, since more diverse the result of classifier only leads to

high accuracy. Aspect based sentiment classification was introduced to analyze the sentiment

of tweet [35]. Herein, ARM was used to extract features that ignore and meanings and

semantics related to the extracted words that tend to reduce the accuracy. PCA was used to

select the features in which covariance matrix computation is very difficult and it cannot

identify small variance in the features.

4. PROPOSED MODEL

This section illustrates the performance of the proposed sentimental analysis in twitter data

with the proposed algorithm in detail.

4.1. System Overview

With the concern of increasing accuracy in sentimental analysis, we performed five

successive processes that are data cleaning, preprocessing, feature extraction, feature selection

and classification as depicted in figure 1. The data cleaning process is performed primarily in

order to increase the efficiency of feature extraction and feature selection process. Here, URL

removal, Username Removal, Punctuation Removal and Spell Correction are performed.

Preprocessing is performed followed by the data cleaning process in order to avoid difficulties

in the twitter dataset. Preprocessing performs the seven sequential processes including

tokenization, stop word removal, acronyms expansion, slang correction, split attached word,

lemmatization and stemming and POS tagging. Feature extraction processes are performed to

increase the classification accuracy in the sentimental analysis. Here, eight features are

extracted namely hashtag based features, standardizing word features, slang related features,

emotional features, semantic features, syntactic feature, Negation features and unigram

features. Feature selection is performed to reduce the difficulties in sentiment classification.

For this purpose, the Dempster Shafer algorithm is proposed which selects an optimal set of



features from the extracted features. At last, we perform sentiment classification using the

OA2-SVM algorithm which classifies tweets into five classes including positive, negative,

strongly positive, strongly negative and neutral.

4.2. Data Cleaning

In this work, we initially perform data cleaning in order to reduce the uncertainty of the

twitter data thus enhances the performance of the upcoming sentimental analysis process. IN

this work, we use WordNet database to execute data cleaning and preprocessing steps.

WordNet is one of the most extensively used thesauri in sentimental analysis. It contains

1,55,000 terms organized in 1,17,597 synsets. Here, synsets represent the group of noun, verb,

adjective and adverb into sets of synonyms. It provides high accuracy in the sentimental

analysis of tweets. Hence, we use the WordNet database to execute data cleaning and

preprocessing steps.

Our data cleaning process comprises of four steps that are described as follows:

4.2.1. URL Removal

URL removal is one of the necessary steps in the data cleaning process which removes

unwanted URL from the tweets since most of the twitter data contains URL in it. It improves

the performance of the sentimental analysis on tweets.

4.2.2. Username Removal

Username removal is defined as the removal of the username from the tweets. Since the

username is the one of the basic characteristics of twitter data which is unnecessary to predict

the sentiment of the tweet. Removal of the username in preprocessing will result in efficient

feature extraction.

Twitter

Database

URL Removal

Username Removal

Spell Correction

Punctuation Removal

Tokenization

Stop Words Removal

Split Attached word

POS tagging

Slang Correction

ACRONMYS Expansion

Lemmatization & Stemming

Data Source

Preprocessing

Hashtag based Features

Emotional Features

Standardizing word Features

Negation Features

Unigram Features

Semantic Features (Doc2vec)

Syntactic Features

Slang features

Data Cleaning

Feature Extraction

Dempster Shafer Algorithm based on

Belief Function

Data Collection

Most Relevant Features

OA2-SVM

Condition Decision

Strongly NegativeStrongly Positive Negative NeutralPositive

SVM1

Sentiment Classification Feature Selection

SVM2 SVMn

Figure 1 Architecture for Proposed Work




4.2.3. Punctuation Removal

Punctuation removal is referred as the removal of punctuations from the tweets. People who

use twitter applications post their tweets with the different punctuation remarks which are not

necessary to analyze the sentiment of the twitter data.

4.2.4. Spell Correction

Spell correction is a noteworthy process in data cleaning since tweets containing more

spelling mistakes. It corrects the spelling of each word in the tweet using the WordNet

database. Thus improves the accuracy of the sentimental analysis on twitter data.

4.3. Preprocessing

Preprocessing is a substantial process in the sentimental analysis of twitter data. It is also

implemented with the aid of the WordNet tool. It performs eight sequential processes that are

discussed as follows:

4.3.1. Tokenization

Tokenization is a primary step in preprocessing which splits the tweets into words, phrases,

symbols and meaningful elements known as tokens. Tokenizing the tweets tends to increase

the efficiency of feature extraction.

4.3.2. Stop Words Removal

Stop words removal is used to remove the stop words from the tweets since it has more stop

words such as the, is, an, about, etc…The reason for removing the stop words is that it doesn‟t

impact on sentimental analysis in twitter data. Removal of stop words will increase in

sentiment classification accuracy.

4.3.3. Split Attached Word

Split attached word is executed to split the attached word. Since twitter data comprises of a lot

of attached words such as Rainyday, Crowheart. The splitting of attached words tends to

easier the feature extraction process in the TSA.

4.3.4. Slang Correction

This process is used to correct the slangs in the tweets. People who use twitter applications

often post their opinions with different slangs, for instance, Happpy, funnnny, Amazzzing and

so on. Correction of slangs will help in the sentiment classification of tweets.

4.3.5. Acronyms Expansion

Acronyms expansion is performed to expand the acronyms in tweets such as OMG, PLS, etc.

These words introduce difficulties in analyzing the sentiment of the tweet. Since acronyms

doesn‟t impact the sentiment of the word accurately. So, we have included acronyms

expansion in a preprocessing step that enhances the performance of the classification.

4.3.6. Stemming and Lemmatization

Stemming and Lemmatization is a sequential process in the preprocessing of twitter data. It is

vital to find the accurate sentiment of the words in the tweets. Stemming is referred as the

process of converting the inflected words into the root word. Lemmatization is referred as the

process of grouping the inflected forms of a word so that they can analyze the single word

accurately.

The main difference between the stemming and lemmatization is that a stemming operates

with a single word without knowledge of the context whereas lemmatization analyzes the

single word accurately with the aid of grouping the inflected words.



4.3.7. POS Tagging

POS tagging is the process of tagging the words in the tweets as corresponding to a precise

part of speech based on both definition and context. Table 4 represents the process before and

after the preprocessing of tweets in the twitter data.

Table 4 Preprocessing Description

Preprocessing

Steps

Before Preprocessing After

Preprocessing

Tokenization It was a Rainyday ‘It’ ‘was’ ‘a’

‘RainyDay’

Stop words

removal

Can listening be exhausting? listening

exhausting

Split Attached

Word

It was a Rainyday It was a Rainy day

Slang Correction Happpppy Sunday Happy Sunday

Acronyms

Expansion

OMG! I wonder O My God! I

Wonder

Stemming and

Lemmatization

Stemming Good

friendship

Good friend

Lemmatization You are not

better than

me

You are not good

than me

POS Tagging Beautiful resting place RB VB JJ NN

4.4. Feature Extraction

Feature extraction is an important process to enhance the classification accuracy in

sentimental analysis. We have extracted features using the Doc2Vec model. Doc2Vec model

performs better than the existing Word2Vec, TF-IDF models. In addition to it, Doc2Vec

model based feature extraction provides high accuracy in ML based classification. It is a

neural network comprises of three layers such as input, hidden and output layers.

In most of the twitter sentimental analysis studies, BoW based feature extraction

techniques are used. The major drawback of BoW based feature extraction is the lack of

proficiency to capture the semantics and syntactic order of the words in the tweets. That also

leads to reduce in accuracy during sentiment classification of tweets. In contrast, Doc2Vec

model is an unsupervised algorithm that able to learn feature vectors for paragraphs, texts and

documents.

The core objective of the Doc2Vec model is to obtain a compressed representation of a

word via predicting the adjacent words in the sentence. It is obtained based on the capture of

the occurrence of the statistical features of words in the sentences and thus obtains new

abundant semantic features. It is operated in two types of modes that are Distributed Memory

and Distributed Bag of Words.

4.4.1. Distributed Memory

Distributed memory is one of the modes in the Doc2Vec model which is inspired by the

Word2Vec model. It is used to preserve the word order in the twitter document. The core idea

of the distributed memory is to predict the center word based on the context word i.e. sampled

set of words.




Document

ID

W(t-1)

W(t+1)

W(t+2)

W(t)

Input Layer

Projection Layer Output Layer

Figure 2 Distributed Memory Mode

Figure 2 represents the architecture of distributed memory mode in with three layers that

are input, projection and output layer.

4.4.2. Distributed Bag of Words

Distributed bag of words is one of the modes in the Doc2Vec model which is slightly

different from the distributed memory model. The distributed Bag of Word model ignores the

context of the word instead it focuses on a model to predict words randomly sampled from the

tweets in the twitter document.

Input Layer Projection Layer

Output Layer

W(t-2)

W(t-1)

W(t+1)

W(t+2)

Figure 3 Distributed Bag of Word Mode

Figure 3 represents the architecture of distributed Bag of Words mode of the Doc2Vec

model which comprises of three types of layers that are input, projection and output.

Table 5 enlists the types of features extracted using the Doc2Vec model along with its example.



Table 5 Types of Features

Feature Example

Hashtag feature #Jail Terrorists

Unigram feature This, was

Standardizing word feature Happpppy

Slang feature Awm,lol

Emotional feature Glad, Sad

Semantic feature Human, adult

Syntactic feature Clever, loud

Negation feature Never, No

Using this Doc2Vec model, we extract sight sequential features that are illustrated as

follows:

4.4.3. Hashtag based Feature

Hashtag based features are used to obtain the sentiment polarity of the tweet. Hashtag in the

tweeter data plays a vital role in classifying the sentiment of the tweets. Since twitter user

includes hashtag keyword before going to deliver their thought in some subject matters. It is

useful to analyze the sentiment of tweets effectually. Example for Hash based feature

extraction is expressed as follows:

Tweet 1 #Jail Terrorists Jail Terrorists

Tweet 2 #WakeUpToRacism Wake Up To Racism

4.4.4. Unigram Feature

Unigram feature is basic in the sentimental analysis of twitter data. This feature performs well

in sentiment classification compared to the level of features extracted from the tweets. It is

extracted with the aid of the proposed Doc2Vec model.

4.4.5. Standardizing Word Feature

Standardizing word is one of the features in the feature extraction process. It is a significant

feature to extract in sentiment analysis since most of the tweets contain words like Happpppy,

Sooooo funnnny, Woooow, etc. These words reflect the sentiment of the tweets. So that, we

extract these features and transform into the root words such as Happy, So funny and WoW in

order to predict the sentiment accurately.

4.4.6. Slang Feature

Slang features are referred as the slang words in the tweets such as beautiful, awesome, laugh

out loud and so on. These words are used to enhance the performance of the classification.

4.4.7. Emotional Feature

Emotional feature plays a major role in sentiment classification of twitter data. It includes

words such as emotions, appraisal, positive words, negative words and sentiment words.

4.4.8. Semantic Feature

Semantic feature is used to represent the basic conceptual components of meaning for any

lexical item. An individual semantic feature organizes one component of word intention

which is the essential sense or concept invoked. Some of the examples for semantic features

are bachelor, adult, girl and so on.

4.4.9. Syntactic Feature

Syntactic feature is used to investigate the word-words relationship in a grammatical way.

Since each word in the tweets is related to the head word and dependent word. This is very

useful during the sentiment classification of tweets.




4.4.10. Negation Feature

Negation is used to find the sentiment orientation of words in the tweet. Usually, negation

features comprise of three classes that are syntactic, diminisher and morphological. Some of

the negation features are listed as follows: Never, Not, No and So on.

4.5. Feature Selection

Feature selection is a substantial process in TSA since it plays a vital role in the reduction of

difficulties in ML based classification. We proposed the Dempster Shafer algorithm which

selects the features that are highly informative and relevant with each class for sentiment

classification. Dempster Shafer algorithm works based on the highest relevance and least

redundancy criterion.

Dempster Shafer algorithm comprises of five major functions that are mass, belief,

plausibility, belief range and Dempster combination rule. These functions are described as

follows:

4.5.1. Mass

Mass function represents the degree of belief which assigns a probability value to each subset

of feature. If is the discernment of feature set , then a mass function is expressed as:

, - ; ( ) And ∑ ( ) ; (1)

4.5.2. Belief

Belief function is used to estimate the importance of each feature in the subset. It is called Bel

in general. Consider a feature set and . The belief committed to the is,

( ) ∑ ( ) ; (2)

Where, ( ) is defined as the strength of the total belief given to the feature set . In

contrast, ( ) is defined as the exact belief given to the feature set . Not like in

probability theory, a prominent characteristic of the Dempster Shafer is that the belief in the

specific hypothesis does not need to imply the remaining belief associated with the negation

of hypothesis. Hence when there is no further available belief is related to the negation of the

hypothesis, the lasting belief is assigned to the entire frame of the discernment.

4.5.3. Plausibility Function

Plausibility function is estimated with the sum of all mass values. Plausibility function of

feature set ( ) is represented as follows:

( ) ∑ ( ) ; (3)

Where represents the feature set to intersect with the feature set .

d) Belief range:

Belief range is defined as the interval between belief and plausibility functions. Plausibility

( ( )) and belief function ( ( )) are related as below expression:

( ) ( ) (4)

4.5.4. Dempster Combination Rule

Dempster Combination Rule is defined as the combination of the joint mass which is

estimated from two sets of mass such as and ,

( ) ∑ ( ) ( )

, - ∑ ( ) ( ) (5)



Where ( ) describes the joint mass of feature set A, ( ) indicates the mass of

feature set B and ( ) represents the mass of feature set C.

Using these functions, Dempster Shafer algorithm selects an optimal feature from the

extracted features. Here, belief and plausibility functions play a major role to select the high

relevant feature for sentiment classification.

4.6. Classification

Classification is a significant process in the sentimental analysis of twitter data. In order to

classify the tweets into multiple classes, we proposed the OA2-SVM algorithm which

classifies the tweets into five classes that are strongly positive, strongly negative, positive,

negative and neutral.

The proposed OA2-SVM is one of the types in the Multi-Class SVM algorithm. This

outperforms the One Against One-SVM so that we have selected this algorithm for sentiment

classification. Assume an -class problem, where we have training

samples: * + * + here is an m-dimensional feature vector and

* + is the corresponding class label. OA2-SVM comprises binary SVM classifiers,

one for each class. Each SVM classifier splits its correspondent class from the other ones. The

multi-classifier output is initiated for the class whose classifier provides the highest result

amongst all. The SVM in the OA2-SVM is trained with all the training samples of the

class with positive labels and all others with negative labels. Mathematically SVM

solves the upcoming problem that yields the decision function ( ) ( ) .

Here, is represented as a weight vector, represented as bias term and ( ) represents the

feature space point.

( )

‖ ‖

∑

( ( ) )

(6)

Where and . At the classification phase,

sample is classified as in class whose generates the largest value which is represented

as follows:

( ) ( ( ) ) (7)

Herein, SVM estimates the sentiment score for each feature using SentiWordNet

ontology in order to classify the sentiment of the tweet accurately. In this, the sentiment score

of each feature is estimated as follows:

( ) ∑ ( )

(8)

Where ( ) represents the sentiment score of feature „k‟.

Figure 4 depicts the flow of OA2-SVM algorithm where five classes are considered

including strong positive (C1), positive (C2), strong negative (C3), negative (C4) and neutral

(C5). To acquire the four class classifier, it needs to consider a set of binary SVM classifiers

such as SVM1, SVM2, SVM 3 and SVM 4 as listed in the figure 4. The classification result is

obtained from the classifier with the maximal output which is an estimated class label for the

current input feature vector.




SVM-1 (C1/others)

SVM-1 (C1/others)

SVM-1 (C1/others)

SVM-1 (C1/others)

C1 (+)

C2 (+)

C3 (+)

C4 (+)

C5 (-)

(-)

(-)

(-)

Classified Result

Selected Features

Figure 4 Flow of OA2-SVM

5. EXPERIMENTAL STUDY

This portion discusses the efficacy of the proposed sentimental analysis. For this purpose, this

section further divided into four parts that are simulation setup, performance metrics and

results comparison.

5.1. Simulation Setup

Our proposed framework is implemented in Netbeans 8.2 Integrated Development

Environment (IDE). Netbeans IDE 8.2 is an open source software development platform that

is envisioned for java applications. Netbeans IDE executed on any operating system which

can support a compatible Java Virtual Machine (JVM). Netbeans platform provides facilities

such as project management, user interface management, storage management and window

management. Table 6 shows the software and hardware requirement of the proposed TSA

system.

Table 6 Software and Hardware Requirement

Software Requirements

Operating System Windows 10

Language Java

IDE Netbeans 8.2

Package XAMPP

Development Kit JDK 1.8

Processor Intel Core

Hardware Requirements

RAM 2GB

Hard disk drive 256GB

In order to evaluate the performance of the proposed TSA, we have used twitter dataset

collected from the online repository. Our dataset comprises of 10,000 tweets with different

sentiment polarities such as strongly positive, positive, strongly negative, negative and

neutral. All experiments are performed in the machine with Intel core with 2GB of RAM

running in 64 bit-Windows 10 operating system



5.2. Performance Metrics

In this section, we evaluate the effectiveness of the proposed TSA using five metrics that are

described briefly as follows:

1. Accuracy: It is used to measure the correctness of the proposed TSA. It is the ratio of the

number of correctly classified instances with the total number of instances. It is expressed as

follows:

(9)

Where, referred as the true positive classifications, referred as the true negative

classification, represented as the false positive classifications and represents the false

negative classification.

2. Precision: It is used to measure the reliability of the proposed TSA. It is designated as the

fraction of the tweets correctly classified with respect to the total number of classified tweets.

The mathematical form of precision is expressed as follows:

(10)

3. Recall: It is defined as the proportion of the tweets correctly classified by the class

(Positive, Negative, Neutral, Strongly Positive, Strongly Negative). It can be represented as

follows:

(11)

4. F-Measure: It is defined as the weighted average of precision and recall. The major

intention of F-measure is to analyze the test‟s accuracy. It is measured as follows:

(12)

5. Error Rate: This metric is used to measure the efficacy of the proposed TSA. It designated

as the ratio of the number of tweets wrongly classified for the total number of tweets. The

numerical representation of the error rate is expressed as follows:

(13)

5.3. Results Comparison

This portion compares the simulation results of the proposed TSA with the existing methods

LAN2FIS, GA and HCS methods. We have selected these methods for comparison since

contributions of this method is similar to the proposed method.

5.3.1. Impact on Accuracy

Accuracy is the most intuitive metric to measure in order to validate the performance of the

TSA. This metric is estimated by varying the number of tweets.

Figure 5 illustrates the comparison of accuracy to the number of tweets. It is noticed that

accuracy for proposed TSA is high compared to the other methods such as LAN2FIS, GA and

HCS methods. Here, we have taken a maximum of 5000 tweets for accuracy comparison. Our

method enhances the accuracy of TSA by improving the efficiency of the twitter database. It

is achieved through performing data cleaning and preprocessing processes. These two

processes remove the uncertainties of twitter database such as acronyms, slang, stop words

and so on.




Figure 5 Comparison of Accuracy

Therefore, our method achieves high accuracy compared to the existing methods.

Whereas, the HCS method achieves less accuracy compared to the other methods since it

performs only a few operations in the preprocessing step that tends to reduce the accuracy.

Likewise, GA and LAN2FIS also achieve less accuracy due to the absence of significant

preprocessing steps that are acronyms expansion and split attached word. As a result, GA and

LAN2FIS methods achieve less accuracy compared to the proposed method. From this

comparison, we conclude that our method increases accuracy up to 25% compared to the

existing methods including LAN2FIS, HCS and GA.

5.3.2. Impact on Precision

Precision is a significant metric to measure the reliability of the proposed TSA. The

performance of the precision is evaluated by varying the number of tweets.

Figure 6 Comparison of Precision

As shown in figure 6, the precision performance of this work is compared with the

existing methods. In this, we have considered the maximum of 5000 tweets for precision

comparison. It is seen that the precision performance of the proposed method is increased as

an increase in the number of tweets. Our method achieves a high precision rate compared to

the existing method. This is attained through the proposed preprocessing step that executes

seven sequential processes using the WordNet database. They are tokenization, stop word

removal, acronyms expansion, slang correction, split attached word, lemmatization and

stemming and POS tagging. Here, acronyms expansion and split attached removal are new

process included in the preprocessing that enhances the efficiency of preprocessing. These

0

20

40

60

80

100

1000 2000 3000 4000 5000

Acc

ura

cy (

%)

# of tweets

HCS GA LAN2FIS Proposed

0

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1000 2000 3000 4000 5000

Pre

cisi

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%)

# of tweets




process easier the performance of sentiment analysis. Thus increases the performance of

precision in TSA. In contrast the existing method HCS achieves less precision percentage

since it only performs noise and stop word removal in the preprocessing step. That increases

the difficulties in feature extraction that reduces the precision. Meanwhile, GA and HCS

methods also attain less accuracy compared to the proposed method. Since, these methods

perform conventional preprocessing method only such as URL removal, slang correction,

etc…that tends to reduce the precision rate. From this comparison, it is observed that our

method increases precision up to 25% compared to the existing methods including LAN2FIS,

HCS and GA.

5.3.3. Impact on Recall

The recall is the noteworthy metric to estimate the correctness of the proposed TSA. It is

measured with the aid of varying the number of tweets. This metric must be high in order to

achieve better performance in sentiment analysis. We have estimated recall percentages with

the consideration of 5000 tweets.

Figure 7 Comparison of Recall

From the figure 7, it is seen that the performance of the proposed method is better than the

existing methods including GA, HCS and LAN2FIS. The reason for this is that our method

extracts optimal features from the preprocessed data. The extracted features are hashtag based

features, standardizing word features, slang related features, emotional features, semantic

features, syntactic feature, Negation features and unigram features. These features extracted

with the assist of the Doc2Vec model which performs better than existing feature extraction

techniques. These features are significant to achieve high accuracy in TSA. Therefore, our

method increases recall compared to the existing methods. Meanwhile, the HCS method

attains a low recall percentage compared to the other methods since it doesn‟t extract

important features such as syntactic, slang related features. As well, GA and LAN2FIS

method also achieves low recall which shows the increase in classification error. This is due

to the fact that lack of features extracted in the feature extraction steps. As a result, GA and

LAN2FIS methods attain a low recall rate compared to our method. From this comparison, we

determine that our method increases recall up to 30% compared to the existing methods

including LAN2FIS, HCS and GA.

5.3.4. Impact on F-Measure

F-Measure is a substantial metric to analyze the performance of the TSA accuracy level. This

metric value should be high in order to increase the efficacy of the TSA performance. We

estimate the F-Measure value via increasing the number of tweets upto 5000.

0

20

40

60

80

100

1000 2000 3000 4000 5000

Rec

all

(%)

# of tweets





Figure 8 Comparison of F-Measure

As depicted in figure 8, the proposed performance in the F-Measure metric is high

compared to the existing methods GA, HCS and LAN2FIS. Since our method proposed an

efficient feature selection technique in order to reduce difficulties in ML based classification.

Indeed, twitter contains huge amounts of features from tweets that affect the performance of

ML based classifier. In order to avoid such discrepancy, we proposed the Dempster Shafer

algorithm based feature selection which selects optimal features from the extracted features

that reduce the feature space also. That, in turn, increases the performance of the ML based

classification. As a result, our method achieves high F-Measure rate compared to the existing

methods. In contrast, the HCS method achieves a high F-Measure rate compared to the other

methods since it doesn‟t perform feature selection during sentiment analysis. Thus, increases

the difficulties in ML based classification that reduces the F-Measure rate. In the meantime,

GA and LAN2FIS performs feature selection before enter into the classification. However,

performances of the feature selection techniques are not upto the level due to performance

degrade when an increase in the data. From this comparison, it is perceived that our method

enhances F-Measure up to 20% compared to the existing methods including LAN2FIS, HCS

and GA.

5.3.5. Impact on Error Rate

Error rate is an important metric to analyze the performance of the proposed classification

method. This metric should be low in order to increase the performance of sentimental

analysis in twitter data. This metric is measured with the aid of an increase in number of

tweets upto 5000.

Figure 9 Comparison of Error Rate

Figure 9 represents the comparison of error rate with the existing methods such as

LAN2FIS, HCS and GA. From this comparison, it is noticed that our method achieves less

0

20

40

60

80

100

1000 2000 3000 4000 5000

F-M

easu

re (

%)

# of tweets


0

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Err

or

Rat

e (%

)

# of tweets




error rate compared to the existing methods. This is achieved through the proposed OA2-SVM

based sentiment classification. OA2-SVM algorithm performs well even for an unstructured

and semi structured data. Thus provides proper results in the classification of twitter sentiment

since twitter also contains in structured data. Furthermore, OA2-SVM doesn‟t perform

complex computation procedure instead it follows a simple procedure to classify the

sentiment of the tweets thus reduces the error rate in sentiment classification. Whereas, the

existing methods like HCS, GA and LAN2FIS method achieve a high error rate compared to

the existing methods. This is due to the fact that complex computation of existing sentiment

classification methods. Therefore, existing methods cannot able to predict the accurate

sentiment of the tweets that increases the error rate in the existing sentiment classification.

From this comparison, it is found that our method reduces error rate up to 29% compared to

the existing methods including LAN2FIS, HCS and GA.

5.3.6. Research Summary

This section summarizes the benefits of the proposed method in terms of the performance

metrics. Our work performs five consequent processes: Data Cleaning, Preprocessing, Feature

Extraction, Feature Selection and Classification.

Data Cleaning: This process reduces the error rate in sentimental analysis of twitter data by

reducing the uncertainties of twitter data.

Preprocessing: This process increases the classification accuracy by performing six sequence

steps that reduce the discrepancy of twitter data. Thus increases the precision rate effectually.

Feature Extraction: This process is used to increases the recall rate via extracting optimal

features. These features are used to reduce the misclassification results in sentimental

analysis.

Feature Selection: It is executed through the Dempster Shafer algorithm which reduces the

feature space effectually that tends to reduce the difficulties in ML based classification.

Therefore, our method enhances the F-Measure.

Classification: It is implemented through the OA2-SVM algorithm which reduces the error

rate owing to its simple procedure in sentiment classification.

From these analyses, we proved that our method performs better in sentimental analysis

on twitter data compared to the existing HCS, GA and LAN2FIS methods.

Table 7 Comparison of Existing and Proposed Results

Method Performance Metrics [Average (%)]

Accuracy Precision Recall F-Measure Error

Rate

HCS 75.2 70 62 71.6 35

GA 80 77 71.6 82.5 30

LAN2FIS 89 88.1 80 89 11

Proposed 93 93.7 92.8 92.8 6

Table 7 describes the comparison of proposed and existing methods respect to the

simulation results. It shows our method outperforms existing methods in term of succeeding

metrics such as Accuracy, Precision, Recall, F-Measure and Error Rate.

6. CONCLUSION

To date, sentimental analysis on social media like twitter, facebook, etc…has drawn much

intention among researchers due to its wide range of application. This paper presents the

sentimental analysis on twitter data based on five consequent processes: Data cleaning,

Preprocessing, Feature extraction, Feature selection and Classification. Data cleaning process




is performed initially in order to reduce the uncertainty of the twitter data. After that,

preprocessing is executed with the concern of increasing accuracy in sentiment classification.

Feature extraction is a significant process in sentimental analysis to attain accurate results in

sentiment classification. To mitigate problems in ML based sentiment classification, we

execute feature selection. Our work adopts Dempster Shafer algorithm to select optimal

features from the extracted features thus reduces the feature space and also diminishes the

difficulties in ML based classification. In regard to attain high accuracy in classification, we

carried out OA2-SVM based sentiment classification which classifies sentiment into five

processes that are positive, negative, strongly positive, strongly negative and neutral.

Performance of this work is evaluated with the aid of the five validation metrics that are

accuracy, precision, recall, f-measure and error rate. From the validation result, we conclude

that our method outperforms all other existing methods including LAN2FIS, HCS and GA. In

the future, we planned to propose sentimental analysis on other social media data such as

facebook, youtube in order to identify the sentiment of the people towards certain issues.

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Documents

TWITTER SENTIMENT ANALYSIS USING DEMPSTER SHAFER …€¦ · issues in the classification of the sentiment [13]. To address this problem, ... lemmatization, spelling correction, stop