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International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 5 Issue 4, May-June 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD42385 | Volume – 5 | Issue – 4 | May-June 2021 Page 758
Twitter Sentiment Analysis
Krunal Dhardev1, Dr. Kamalraj R2
1Student, 2Associate Professor,
1,2School of CS and IT, Jain University, Bangalore, Karnataka, India
ABSTRACT
Microblogging today has gotten an acclaimed specific instrument among
Internet clients. Endless clients share assessments on various bits of life
dependably. Accordingly, microblogging districts are rich wellsprings of
information for assessment mining and tendency assessment. Since
microblogging has shown up by and large lately, there several investigation
works that were given to this point. In our paper, we base on using Twitter,
the most notable microblogging stage, for the task of feeling examination. We
advise the most ideal approach to thus accumulate a corpus for assessment
and evaluation mining purposes. We play out a semantic assessment of the
amassed corpus and clarify found wonders. Utilizing the corpus, we build up
an end classifier, that can pick positive,negative,andhonestevaluationsforan
annual. Test assessments show that our proposed strategies are convincing
and act in a way that is better than actually proposed procedures. In our
appraisal, we worked with English,inanycase,theproposedprocedurecanbe
utilized with some other language.
KEYWORDS: social media; Sentiment Analysis; Twitter
How to cite this paper: Krunal Dhardev |
Dr. Kamalraj R "Twitter Sentiment
Analysis" Published
in International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456-
6470, Volume-5 |
Issue-4, June 2021,
pp.758-761, URL:
www.ijtsrd.com/papers/ijtsrd42385.pdf
Copyright © 2021 by author (s) and
International Journal ofTrendinScientific
Research and Development Journal. This
is an Open Access article distributed
under the terms of
the Creative
Commons Attribution
License (CC BY 4.0)
(http://creativecommons.org/licenses/by/4.0)
1. INTRODUCTION
Assessment and nostalgic mining are significant
exploration territories in light of the fact that becauseof
the gigantic number of day-by-day posts on
interpersonal organizations, separating individuals'
feelings is a difficult errand. Around 90% of the present
information has been given during the most recent two
years and getting knowledge into this enormous scope
of information isn't unimportant
The nostalgic examination has various applications for
different regions for example in associations to get
reactions for things by which associations can get
comfortable with customer's information and reviews
by means of online media.
Appraisal and nostalgic mininghavebeena lotofpacked
in this reference and each different technique and
investigation fields have been discussed. There are
likewise a few works that have been done on Facebook
nostalgic investigation anyway in this paper we
generally canter around the Twitter wistful
examination.
For bigger writings, one arrangement could be to
comprehend the content, sum up it, and offer load to it
whether it is positive, negative, or impartial. Two
essential ways to deal with remove text rundown are
extractive and abstractive techniques. In the extractive
technique, words and word phrases are removed from
the first content to produce a rundown.Inanabstractive
strategy, attempts to get familiar with an inward
language portrayal andafterwardcreatesanoutlinethat
is more like the synopsis done by people.
Text understanding is a huge issue to tackle. Some AI
procedures, including different directed and unaided
calculations, are being used. There are various ways to
deal with produce an outline. Onemethodologycouldbe
to rank the significance of sentences inside the content
and afterward create a rundown for the content
dependent on the significant numbers. There is another
methodology called start to finish generative models. In
a few an area like picture acknowledgment, discourse
acknowledgment,languageinterpretation,andquestion-
replying, the start to finish strategy performs better.
A couple of works have used cosmology to fathom the
substance. At the articulation level, the contemplative
examination system should have the alternative to see
the furthest point of the articulation which is discussed
by Wilson, et.al. Tree part and feature-basedmodel have
been applied for insightful examination in Twitter by
Agarwal and et.al. SemEval-2017 also shows the seven
years of nostalgic examination in Twitter tasks. Since
tweets on Twitter is a specificversionofa common book
there are a couple of works that address this issue like
the work for short easy-going compositions. The
nostalgic examination has numerous applications in
news.
In this paper, we will talk about interpersonal
organization examination and its significance, at that
point we talk about Twitter as a rich asset for nostalgic
investigation. In the accompanying areas, we show the
significant level unique of our execution. We will showa
few questions on various points and show theextremity
of tweets.
IJTSRD42385
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD42385 | Volume – 5 | Issue – 4 | May-June 2021 Page 759
Notion Analysis is a strategy generally utilized in text
mining. Opinion Analysis is an NLP and data extraction
task that expects to acquire essayists felling
communicated in certain or negative remarks,
questions, and demandsbybreakingdownanenormous
number of archives.
As a rule, notion investigation expects to decide the
demeanour of a speaker or essayist as for some theme
or the general usefulness of the report. Fundamentally,
supposition Analysis is the undertaking of recognizing
whether the assessment communicated in content is
positive or negative.
We utilize a dataset shaped by gathered messages from
Twitter. Twitter contains an enormous number of
extremely short messages made by the clients of this
microblogging stage. The substance of the messages
change from individual contemplations to public
proclamations.
Ideological groups might be intrigued to know whether
individuals support their program or not. Social
associations may ask individuals' feelings on current
discussions. This data can be acquired from
microblogging administrations,astheirclientsposteach
day what they like/hate, and their suppositions on
numerous parts of their life.
In our paper, we concentrate on how microbloggingcan
be utilized for notion examination purposes. We tell the
best way to utilize Twitter as a corpus for estimation
examination and assessment mining.
2. DATA CAPTURING AND PROCESSING
2.1. Data Capturing Process:
Twitter streaming API is utilized to catch the information.
Twitter real time API assists with making associations
between PC projects and web administrations.Forgettingto
Twitter streaming API we need four keys called API Key,API
Secret, Access Token, and Access Token Secret. Steps to
recover four keys
Create a Twitter account
Open page https://apps.twitter.com/ and login with
twitter credentials
Try creating a new app
Fill the form and ‘Create twitter new application’
Retrieve API keys and API secret
Retrieve access token and Access token secret.
Once all four keys are retrieved, I have used a python library
called Tweepy to download the tweets. Tweepyisconnected
to twitter streaming API to retrieve particular productlatest
post by default I set as 25 but we can increase the no of post
we want.
Here is the example code used to retrieve resent 25 tweets
from Twitter.
Figure 1. Example Code Tweet Retrieval
2.2. Data Processing:
Tweets are caught in a Data outline. Tweets are characterized into 3 classes. Positive, negative, and neutral as noticed
previously. Table 1 show some illustration of how tweets would be arranged in their classifications.
Table 1 Tweets Classification Example
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD42385 | Volume – 5 | Issue – 4 | May-June 2021 Page 760
2.2.1. Pre-Processing :
Each tweet goes through the accompanying pre-handling
steps:
1. Remove unnecessary data :
Removing word that contains @
Removing '#' hash tag
Removing hyperlink (https:)
Removing RT (retweet)
Remove n
Remove :
Remove both the leading and the trailing characters
Removes empty strings, because they are considered in
Python as False
sub() strategy from python's standard articulationclasswas
utilized to substitute URLs, usernames, void areas, hashtags
with the pertinent qualities as clarified previously. The
strip() technique from string class is utilized to strip the
leftover expressions of any accentuation.
2.2.2. Obtaining Stop Words List:
A stop word is an ordinarily utilized word, (for example,
"the", "a", "an", "in") that an internet searcher has been
customized to disregard, both when ordering sections for
looking and while recovering them as the consequence of an
inquiry question.
We would not need these words to occupy room in our data
set, or occupying the important preparing time. For this, we
can eliminate them effectively,byputtingawaya rundownof
words that you consider to stop words. NLTK(Natural
Language Toolkit) in python has a rundown of stopwords
put away in 16 unique dialects.
2.2.3. Stemming:
Stemming is the route toward perceivinginducedwords and
consigning a word to all of the decided words. This will
diminish the size of the rundown reports. The basic tweet
which is in the string configuration is changed over into a
python once-over of substringswhichcanbeusedtoprocure
all of the words and highlight in the tweet. The NLTK
Tokenizer Package is usedthus.Thehiddenstringisdecoded
to utf8 to make an effort not to manage encoded strings.
NLTK library as of now gives an execution of the Porter
stemmer computation in the nltk. stem. porter module. The
tokenized string is used as a commitment to the Porter
stemmer.
Methodology:
1. tf-idf:
tf-idf represents Term recurrence opposite report
recurrence. The tf-idf weight is a weight frequently utilized
in data recovery and text mining. Varieties of the tf-idf
weighting plan are regularly utilized via web crawlers in
scoring and positioning a report's pertinence given a
question. This weight is a factual measure used to assess
how significant a word is to a report in an assortment or
corpus. The significance builds relatively to the occasions a
word shows up in the archive yet is balanced by the
recurrence of the word in the corpus (informational index).
tf-idf is a weighting plan that appoints each term in a record
a weight dependent on its term recurrence (tf) and
backwards report recurrence (idf). The terms with higher
weight scores are viewed as more significant.
2. Bag of words :
The pack of-words model is an improving on portrayal
utilized in normal language handling and data recovery(IR).
In this model, a content (like a sentence or a record) is
addressed as the pack (multiset) of its words, dismissing
syntax and even word request yet keeping assortment. The
pack of-words model has additionally been utilized for PC
vision.
The sack of-words model is usually utilized in strategies for
record grouping where the (recurrence of) event of each
word is utilized as an element for preparing a classifier.
OBJECTIVES
Sentiment analysis over Twitter offer organisations a fast
and effective way to monitor the publics' feelings towards
their brand, business, directors, etc. A wide rangeoffeatures
and methods for training sentiment classifiers for Twitter
datasets have been researched in recent years with varying
results.
Business
Politics
Public Actions
CONCLUSION:
In this specific paper, we inspected the meaning of
casual local area examination and its applications in
different areas. We focused on Twitter and have
executed the python program to do the insightful
assessment. We showed the resultsondifferentstep-by-
step subjects. We comprehended that the impartial
speculations are in a general sense high which shows
there is a need to improve Twitter thoughtexamination.
Twitter conclusion examination is created to research
the public's perspectives towards a tweet/hashtag. Info
is given i.e., either the username or a hashtag. At that
point, the tweet is recovered from twitter data thatgoes
through highlight extraction. PartnerinNursingprudent
element vector is framed by doing highlight extraction
in 2 stages when right pre-handling. inside the
beginning, theTwitter-explicitalternativesterritoryunit
removed and added to the component vector. Fromthat
point forward, these alternatives region unit detached
from tweets, and again highlight extraction is done as
though it's done on conventional content. These
alternatives are extra to the element vector.
Characterization precision of the component vector is
tried exploitation Naïve Thomas Bayes classifier.
Partner in Nursing precision of 78. 38 it had been
reached.
Future scope:
It can implement on social media like Facebook and
Instagram, and also try to capture past post.one more thing
that I can implement in future. That thing is if any person
post a negative tweets we can try to change diplomatic
sentence. For diplomatic sentence we will use neural
networks.
Acknowledgement:
Foremost, I would like to thank Dr.M. N Nachappa and Pro.
Kamalraj R for the continuous support of my MCA research
paper, and for their knowledge, patience, and motivation
throughout the year. Your guidance helped me in research
and writing this thesis, their convenient course, total co-
activity, and moment perception have made my work
productive.
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD42385 | Volume – 5 | Issue – 4 | May-June 2021 Page 761
References
[1] Agarwal, A., Xie, B., Vovsha, I., Rambow, O.,
Passonneau, R.: Sentiment analysis of twitter data
[2] Guerra, P., Veloso, A., Meira Jr, W., Almeida, V.: From
bias to opinion: A transfer-learning approach to real-
time sentiment analysis.
[3] Guerra, P., Veloso, A., Meira Jr, W., Almeida, V.: From
bias to opinion: A transfer-learning approach to real-
time sentiment analysis.
[4] Kouloumpis, E., Wilson, T., Moore, J.: Twitter
sentiment analysis: The good the badandtheomg!In:
Proceedings of the ICWSM.s
[5] David Vilares, Yerai Doval, Miguel A. Alonso, and
Carlos Gomez-Rodrıguez.
[6] Adil Moujahid, An Introduction to Text Mining using
Twitter Streaming API and Python
[7] Sunil Ray, September 2017, Understanding Support
Vector Machine Algorithm from Examples

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Twitter Sentiment Analysis

  • 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 5 Issue 4, May-June 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD42385 | Volume – 5 | Issue – 4 | May-June 2021 Page 758 Twitter Sentiment Analysis Krunal Dhardev1, Dr. Kamalraj R2 1Student, 2Associate Professor, 1,2School of CS and IT, Jain University, Bangalore, Karnataka, India ABSTRACT Microblogging today has gotten an acclaimed specific instrument among Internet clients. Endless clients share assessments on various bits of life dependably. Accordingly, microblogging districts are rich wellsprings of information for assessment mining and tendency assessment. Since microblogging has shown up by and large lately, there several investigation works that were given to this point. In our paper, we base on using Twitter, the most notable microblogging stage, for the task of feeling examination. We advise the most ideal approach to thus accumulate a corpus for assessment and evaluation mining purposes. We play out a semantic assessment of the amassed corpus and clarify found wonders. Utilizing the corpus, we build up an end classifier, that can pick positive,negative,andhonestevaluationsforan annual. Test assessments show that our proposed strategies are convincing and act in a way that is better than actually proposed procedures. In our appraisal, we worked with English,inanycase,theproposedprocedurecanbe utilized with some other language. KEYWORDS: social media; Sentiment Analysis; Twitter How to cite this paper: Krunal Dhardev | Dr. Kamalraj R "Twitter Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456- 6470, Volume-5 | Issue-4, June 2021, pp.758-761, URL: www.ijtsrd.com/papers/ijtsrd42385.pdf Copyright © 2021 by author (s) and International Journal ofTrendinScientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0) 1. INTRODUCTION Assessment and nostalgic mining are significant exploration territories in light of the fact that becauseof the gigantic number of day-by-day posts on interpersonal organizations, separating individuals' feelings is a difficult errand. Around 90% of the present information has been given during the most recent two years and getting knowledge into this enormous scope of information isn't unimportant The nostalgic examination has various applications for different regions for example in associations to get reactions for things by which associations can get comfortable with customer's information and reviews by means of online media. Appraisal and nostalgic mininghavebeena lotofpacked in this reference and each different technique and investigation fields have been discussed. There are likewise a few works that have been done on Facebook nostalgic investigation anyway in this paper we generally canter around the Twitter wistful examination. For bigger writings, one arrangement could be to comprehend the content, sum up it, and offer load to it whether it is positive, negative, or impartial. Two essential ways to deal with remove text rundown are extractive and abstractive techniques. In the extractive technique, words and word phrases are removed from the first content to produce a rundown.Inanabstractive strategy, attempts to get familiar with an inward language portrayal andafterwardcreatesanoutlinethat is more like the synopsis done by people. Text understanding is a huge issue to tackle. Some AI procedures, including different directed and unaided calculations, are being used. There are various ways to deal with produce an outline. Onemethodologycouldbe to rank the significance of sentences inside the content and afterward create a rundown for the content dependent on the significant numbers. There is another methodology called start to finish generative models. In a few an area like picture acknowledgment, discourse acknowledgment,languageinterpretation,andquestion- replying, the start to finish strategy performs better. A couple of works have used cosmology to fathom the substance. At the articulation level, the contemplative examination system should have the alternative to see the furthest point of the articulation which is discussed by Wilson, et.al. Tree part and feature-basedmodel have been applied for insightful examination in Twitter by Agarwal and et.al. SemEval-2017 also shows the seven years of nostalgic examination in Twitter tasks. Since tweets on Twitter is a specificversionofa common book there are a couple of works that address this issue like the work for short easy-going compositions. The nostalgic examination has numerous applications in news. In this paper, we will talk about interpersonal organization examination and its significance, at that point we talk about Twitter as a rich asset for nostalgic investigation. In the accompanying areas, we show the significant level unique of our execution. We will showa few questions on various points and show theextremity of tweets. IJTSRD42385
  • 2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD42385 | Volume – 5 | Issue – 4 | May-June 2021 Page 759 Notion Analysis is a strategy generally utilized in text mining. Opinion Analysis is an NLP and data extraction task that expects to acquire essayists felling communicated in certain or negative remarks, questions, and demandsbybreakingdownanenormous number of archives. As a rule, notion investigation expects to decide the demeanour of a speaker or essayist as for some theme or the general usefulness of the report. Fundamentally, supposition Analysis is the undertaking of recognizing whether the assessment communicated in content is positive or negative. We utilize a dataset shaped by gathered messages from Twitter. Twitter contains an enormous number of extremely short messages made by the clients of this microblogging stage. The substance of the messages change from individual contemplations to public proclamations. Ideological groups might be intrigued to know whether individuals support their program or not. Social associations may ask individuals' feelings on current discussions. This data can be acquired from microblogging administrations,astheirclientsposteach day what they like/hate, and their suppositions on numerous parts of their life. In our paper, we concentrate on how microbloggingcan be utilized for notion examination purposes. We tell the best way to utilize Twitter as a corpus for estimation examination and assessment mining. 2. DATA CAPTURING AND PROCESSING 2.1. Data Capturing Process: Twitter streaming API is utilized to catch the information. Twitter real time API assists with making associations between PC projects and web administrations.Forgettingto Twitter streaming API we need four keys called API Key,API Secret, Access Token, and Access Token Secret. Steps to recover four keys Create a Twitter account Open page https://apps.twitter.com/ and login with twitter credentials Try creating a new app Fill the form and ‘Create twitter new application’ Retrieve API keys and API secret Retrieve access token and Access token secret. Once all four keys are retrieved, I have used a python library called Tweepy to download the tweets. Tweepyisconnected to twitter streaming API to retrieve particular productlatest post by default I set as 25 but we can increase the no of post we want. Here is the example code used to retrieve resent 25 tweets from Twitter. Figure 1. Example Code Tweet Retrieval 2.2. Data Processing: Tweets are caught in a Data outline. Tweets are characterized into 3 classes. Positive, negative, and neutral as noticed previously. Table 1 show some illustration of how tweets would be arranged in their classifications. Table 1 Tweets Classification Example
  • 3. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD42385 | Volume – 5 | Issue – 4 | May-June 2021 Page 760 2.2.1. Pre-Processing : Each tweet goes through the accompanying pre-handling steps: 1. Remove unnecessary data : Removing word that contains @ Removing '#' hash tag Removing hyperlink (https:) Removing RT (retweet) Remove n Remove : Remove both the leading and the trailing characters Removes empty strings, because they are considered in Python as False sub() strategy from python's standard articulationclasswas utilized to substitute URLs, usernames, void areas, hashtags with the pertinent qualities as clarified previously. The strip() technique from string class is utilized to strip the leftover expressions of any accentuation. 2.2.2. Obtaining Stop Words List: A stop word is an ordinarily utilized word, (for example, "the", "a", "an", "in") that an internet searcher has been customized to disregard, both when ordering sections for looking and while recovering them as the consequence of an inquiry question. We would not need these words to occupy room in our data set, or occupying the important preparing time. For this, we can eliminate them effectively,byputtingawaya rundownof words that you consider to stop words. NLTK(Natural Language Toolkit) in python has a rundown of stopwords put away in 16 unique dialects. 2.2.3. Stemming: Stemming is the route toward perceivinginducedwords and consigning a word to all of the decided words. This will diminish the size of the rundown reports. The basic tweet which is in the string configuration is changed over into a python once-over of substringswhichcanbeusedtoprocure all of the words and highlight in the tweet. The NLTK Tokenizer Package is usedthus.Thehiddenstringisdecoded to utf8 to make an effort not to manage encoded strings. NLTK library as of now gives an execution of the Porter stemmer computation in the nltk. stem. porter module. The tokenized string is used as a commitment to the Porter stemmer. Methodology: 1. tf-idf: tf-idf represents Term recurrence opposite report recurrence. The tf-idf weight is a weight frequently utilized in data recovery and text mining. Varieties of the tf-idf weighting plan are regularly utilized via web crawlers in scoring and positioning a report's pertinence given a question. This weight is a factual measure used to assess how significant a word is to a report in an assortment or corpus. The significance builds relatively to the occasions a word shows up in the archive yet is balanced by the recurrence of the word in the corpus (informational index). tf-idf is a weighting plan that appoints each term in a record a weight dependent on its term recurrence (tf) and backwards report recurrence (idf). The terms with higher weight scores are viewed as more significant. 2. Bag of words : The pack of-words model is an improving on portrayal utilized in normal language handling and data recovery(IR). In this model, a content (like a sentence or a record) is addressed as the pack (multiset) of its words, dismissing syntax and even word request yet keeping assortment. The pack of-words model has additionally been utilized for PC vision. The sack of-words model is usually utilized in strategies for record grouping where the (recurrence of) event of each word is utilized as an element for preparing a classifier. OBJECTIVES Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide rangeoffeatures and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. Business Politics Public Actions CONCLUSION: In this specific paper, we inspected the meaning of casual local area examination and its applications in different areas. We focused on Twitter and have executed the python program to do the insightful assessment. We showed the resultsondifferentstep-by- step subjects. We comprehended that the impartial speculations are in a general sense high which shows there is a need to improve Twitter thoughtexamination. Twitter conclusion examination is created to research the public's perspectives towards a tweet/hashtag. Info is given i.e., either the username or a hashtag. At that point, the tweet is recovered from twitter data thatgoes through highlight extraction. PartnerinNursingprudent element vector is framed by doing highlight extraction in 2 stages when right pre-handling. inside the beginning, theTwitter-explicitalternativesterritoryunit removed and added to the component vector. Fromthat point forward, these alternatives region unit detached from tweets, and again highlight extraction is done as though it's done on conventional content. These alternatives are extra to the element vector. Characterization precision of the component vector is tried exploitation Naïve Thomas Bayes classifier. Partner in Nursing precision of 78. 38 it had been reached. Future scope: It can implement on social media like Facebook and Instagram, and also try to capture past post.one more thing that I can implement in future. That thing is if any person post a negative tweets we can try to change diplomatic sentence. For diplomatic sentence we will use neural networks. Acknowledgement: Foremost, I would like to thank Dr.M. N Nachappa and Pro. Kamalraj R for the continuous support of my MCA research paper, and for their knowledge, patience, and motivation throughout the year. Your guidance helped me in research and writing this thesis, their convenient course, total co- activity, and moment perception have made my work productive.
  • 4. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD42385 | Volume – 5 | Issue – 4 | May-June 2021 Page 761 References [1] Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data [2] Guerra, P., Veloso, A., Meira Jr, W., Almeida, V.: From bias to opinion: A transfer-learning approach to real- time sentiment analysis. [3] Guerra, P., Veloso, A., Meira Jr, W., Almeida, V.: From bias to opinion: A transfer-learning approach to real- time sentiment analysis. [4] Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: The good the badandtheomg!In: Proceedings of the ICWSM.s [5] David Vilares, Yerai Doval, Miguel A. Alonso, and Carlos Gomez-Rodrıguez. [6] Adil Moujahid, An Introduction to Text Mining using Twitter Streaming API and Python [7] Sunil Ray, September 2017, Understanding Support Vector Machine Algorithm from Examples