VIRTUAL EMOTION DETECTION BY SENTIMENT
ANALYSIS
Rahul Kamdi
Assistant Professor, Yeshwantrao Chavan College of Engineering, Nagpur Maharashtra, (India).
Prasheel N. Thakre
Assistant Professor, Shri Ramdeobaba College of Engineering and Management, Nagpur,
Maharashtra, (India).
Ajinkya P. Nilawar
Assistant Professor, Shri Ramdeobaba College of Engineering and Management, Nagpur,
Maharashtra, (India).
J. D. Kene
Assistant Professor, Shri Ramdeobaba College of Engineering and Management, Nagpur,
Maharashtra, (India).
E-mail: jagdish.kene@gmail.com
Reception: 15/11/2022 Acceptance: 30/11/2022 Publication: 29/12/2022
Suggested citation:
Kamdi, R., Thakre, P. N., Nilawar, A. P., y Kane, J. D. (2022). Virtual emotion detection by sentiment analysis.
3C TIC. Cuadernos de desarrollo aplicados a las TIC, 11(2), 175-181. https://doi.org/
10.17993/3ctic.2022.112.175-181
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ABSTRACT
As websites, social networks, blogs, and online portals proliferate on the internet, authors are
producing reviews, opinions, ideas, ratings, and feedback. The emotional content of this writer may be
about things like books, people, hotels, items, studies, events, and so on. These emotions have great
value for businesses, for governments, and for people. The majority of the writer-generated material
requires the usage of text mining algorithms and sentiment analysis, even if this information is meant
to be instructive. Sentiment Analysis is a technique in Natural Language Processing (NLP) that tries
to identify and extract assessments communicated within a given text. This paper intends to execute
different content handling strategies in NLP and use of Valence Aware Dictionary for Sentiment
Reasoning (VADER) Model that is sensitive to both polarity (positive/negative) and intensity (strength)
of emotion.
KEYWORDS
Natural Language Processing (NLP), Polarity Intensity, Sentiment Analysis, Virtual Emotion
Detection, VADER.
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1. INTRODUCTION
The emotions are of primarily of six types - love, happiness, anger, sadness, surprise and fear.
Sentiment analysis is used to analyze the emotions present in a text. It is a method for determining if a
given piece of writing is positive, negative, or neutral. The goal of sentiment analysis is to determine
the writer's attitude, sentiments, and emotions in a written text using a computational treatment of
subjectivity in the text. By application of sentiment analysis one can decipher the given sentence,
paragraph or a document contains a positive or negative emotions or expressions in it. In sentiment
analysis we classify the polarity of given text, it results by telling about opinion whether it’s positive,
negative or neutral.
2. LITERATURE SURVEY
Sentiment analysis has multiple ways and Vader is one of the best way (Mozetic et al., 2016) which is
being used. Vader stands for Valence Aware Dictionary and Sentiment Reasoning. It works on a ruled
based sentiment analysis and it contains list of lexical features which are labeled as per semantic
orientation. By analyzing the intensity of wordings in the text, the sentiment score can be obtained of
that text. Vader is smart enough to extract meaning of these words or texts as positive sentiments and
words like Sad, bad, awful as negative sentiments. Vader only cares about the expressions in the text.
Opinions like positive negative or neutral are the expression which is concerned for Vader.
Steven Bird & Loper (2009) express when a text is changed into its canonical or standard form, then it
is called as Text normalization. A few processes have to be done to standardize the content and convert
it into fitting structure which would then be given to the machine learning (ML) model. This helps in
reducing unnecessary information that the computer does not require, thus subsequently improving
efficiency. Library utilized for this is given in. Steps associated with this cycle are shown in Figure 1
and explained briefly in the following sections.
Fig 1. Text Normalization.
2.1 REMOVING STOPWORDS
A stopword is an ordinarily utilized word that can be disregarded, both when ordering sections for
looking and while recovering them (Saif et al., 2014). Using pre-compiled stopword lists or more
complex algorithms for dynamic stopword recognition, removing stopwords from textual data is a
popular procedure for reducing noise. The Natural Language Toolkit (NLTK) in Python includes a list
of stopwords for 16 different dialects.
2.2 TOKENIZATION
The method of breaking text into smaller units called tokens is commonly known as Tokenization
(Stanford NLP Group, 2015). Tokens can be words, characters, or subwords in this case. As a result,
tokenization can be divided into three categories: word, character, and sub-word tokenization.
2.3 DERIVING ROOT WORD
Nicolai & Kondrak (2016) explains in the zones of Natural Language Processing we come across
circumstances where a word has many offshoots Stemming and lemmatization are the two main NLP
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processes for generating root words. The root type of an inflected word is produced by both stemming
and lemmatization (Samir & Lahbib, 2018). Stemming computation works by removing the word's
postfix. Lemmatization thinks about morphological examination of the words. It restores the lemma
which is the base type of all its inflectional structures.
2.4 FEATURE EXTRACTION
Machine learning calculations are unable to work on Crude content legitimately. The procedure of
component extraction requires converting content into matrix or in vector form. The module can be
used to extract features from database consisting of formats such as text and images and extract
features in a format supported by machine learning algorithms the most popular strategies that
includes feature extraction are Bag-of-Words and TF-IDF Vectorizer. Normalize with diminishing
important tokens that appears in majority samples/documents (Mahajan et al., 2020).
2.5 POLARITY AND INTENSITY SCORE IN EMOTIONAL ANALYSIS
A key element of emotional analysis is to examine the body of a text to understand the concept it
expresses. Emotional analysis is appropriate for positive or negative values, known as polarity.
The perfect situation usually ends up being good, neutral or bad with the help of a polarity point
calculation. In general, emotional analysis works better in a submissive text than in a single
context text of purpose. Emotional analysis is widely used, as part of the analysis of social media
in any domain, to understand the functioning of any system, to be controlled by people and that
their response is based on their opinions.
Text textual analysis data can be calculated at most levels, either at the level of each sentence, at
the paragraph level, or throughout the document. There are two major theories in emotional
analysis. First is Learning Prescribed machine reading or in-depth learning: In this approach,
traditional machine learning techniques with a TF-IDF model using the n-gram method. These
divisions are the mindless Bayes of many lands, the orderliness of things, the closest neighbor k
and the uninhabited forest. In all four classes, orderliness is achieved with minimal accuracy.
Second is unsupported dictionary control: This method is to use a large learning process. The
accuracy we get from reading a lot is much less than how to learn by machine. After obtaining
excellent performance and fragmentation, the next step is to create a final model for back-to-work
using certain advanced machine learning methods.
2.6 VALENCE AWARE DICTIONARY FOR SENTIMENT REASONING
(VADER)
VADER is a model used for analysis of text sentiment from which it can detect both polarity
(positive/negative) and emotion intensity or strength. VADER majorly relies on a dictionary that
matches the lexical features to emotion intensities also known as sentiment scores for sentimental
analysis (Beri, 2020). By summing up the intensity of each word in the text we can get the
sentiment score of a text. Sentiment analysis statistically detects whether the polarity of a piece of
text is negative or positive. Sentimental analysis is based on two approaches: polarity-based
analysis, in which texts are classed as either negative or positive, and valence-based analysis, in
which the intensity of the emotion or sentiment is considered.
2.7 WORKING OF VADER
VADER is a sentiment or emotion analysis method that uses lexicons of sentiment-related words.
Each word in the lexicon is classed as positive or negative, and the strength of positivity or
negativity is also examined using this method. Table 1 depicts the sentiment rating of an excerpt
from VADER's lexicon, with higher positive ratings for more positive words and lower negative
ratings for more negative terms.
Table I. Sentiment rating of the various words in a text.
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According to Hutto & Gilbert in 2014, to determine if these terms are favorable or negative, a group of
individuals must personally rate them, which is both time-consuming and costly (Hutto & Gilbert,
2014). The lexicon must contain a good coverage of the words of interest in your text; otherwise,
accuracy will be poor. When the lexicon and the text are well-matched, this method is quite precise,
and it even produces speedy results on vast volumes of text. VADER not only matches the words in
the text with its lexicon, but it also takes into account certain aspects of the way the words are written
as well as their context meaning.
3. EXPERIMENTATION AND PERFORMANCE ANALYSIS
In this paper, sentiment analysis by the utilization of VADER is performed. Firstly, utilize
standardization methods in Natural Language Processing (NLP) for converting the test text into its
vector form. The implementation of emotion analysis evaluated in Python using Natural Language
Toolkit (NLTK) library. Figure 2 shows the virtual emotion analysis of the text where the emotional
content is adjudged as 75% positive. Figure 3 shows the virtual emotion analysis of the text where the
emotion is adjudged as 26% positive which is shown in red color depicting 26% negative. Figure 4
shows the sentiment analysis of long text where the sentiment is adjudged as 92% positive. By adding
this feature of detecting gibberish, we are able to detect gibberish text where the algorithm returns
50% as can be seen in Figure 5.
Fig 2. Virtual emotion detection of text showing 75% positive polarity.
Word Sentiment rating
tragedy -3.3
rejoiced 2.0
insane -1.6
disaster -3.2
great 3.3
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Fig 3. Virtual emotion detection of text showing 26% negative polarity.
Fig 4. Virtual emotion detection of long text showing 92% positive polarity.
Fig 5. Emotion detection of gibberish text.
4. CONCLUSION
The main focus of this paper is to calculate two scores of text: polarity and its intensity by using
machine learning. The polarity range is between -1 to 1(negative to positive) and which help us to find
whether the text is positive or negative. In this paper we have tested different emotions of a text by
using natural language processing toolkit in python and quantified this emotion as positive or negative.
Also we were able to identify a gibberish text i.e. words which does not make any sense or are
rubbish.
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