A Comparison of Automatic Labelling Approaches for Sentiment
Sumana Biswas, Karen Young and Josephine Griffith
School of Computer Science, National University of Ireland, Galway, Ireland
Sentiment Analysis, NLP, Deep Learning, Automatic Labelling.
Labelling a large quantity of social media data for the task of supervised machine learning is not only time-
consuming but also difficult and expensive. On the other hand, the accuracy of supervised machine learning
models is strongly related to the quality of the labelled data on which they train, and automatic sentiment
labelling techniques could reduce the time and cost of human labelling. We have compared three automatic
sentiment labelling techniques: TextBlob, Vader, and Afinn to assign sentiments to tweets without any human
assistance. We compare three scenarios: one uses training and testing datasets with existing ground truth
labels; the second experiment uses automatic labels as training and testing datasets; and the third experiment
uses three automatic labelling techniques to label the training dataset and uses the ground truth labels for
testing. The experiments were evaluated on two Twitter datasets: SemEval-2013 (DS-1) and SemEval-2016
(DS-2). Results show that the Afinn labelling technique obtains the highest accuracy of 80.17% (DS-1) and
80.05% (DS-2) using a BiLSTM deep learning model. These findings imply that automatic text labelling could
provide significant benefits, and suggest a feasible alternative to the time and cost of human labelling efforts.
Social media facilitates the sharing of ideas, views,
and emotional responses between people. In these
interactions, people often use short-text, meaningless
unofficial words, short words, and emoticons, which
can make it confusing to understand the exact mean-
ing of the text. Sentiment analysis can be defined as a
technique for classifying emotions into binary (Posi-
tive, and Negative) or ternary (Positive, Negative, and
Neutral) classes. Extracting sentiment from this typ-
ically unstructured social media data is challenging
and presents a difficult problem for researchers in this
space. Analysing opinions is important because it can
provide useful information for specific products, per-
spectives, or commentary on world events. Success-
ful sentiment analysis of textual information (Daniel
et al., 2017; Xu et al., 2019) depends on three main
elements: a meaningful and clear expression of con-
text; correctly labelled training and test datasets; and
a suitable machine learning algorithm capable of ac-
curately characterizing sentiment.
The labelling of social media data is an open
problem for researchers when analysing sentiment.
Although, it is easy and cheap to get labelled data
from some online crowdsourcing systems like Rent-
A-Coder, Amazon Mechanical Turk, etc. (Snow et al.,
2008; Whitehill et al., 2009), in many cases, one has
no idea about the availability, efficiency, and quality
of the labellers for a specific field or task. Labelling
errors from non-expert labellers, and biased labelling
due to a lack of efficiency, can result in incorrect and
imbalanced labelling. The alternative, to ensure high-
quality labels, is to engage human experts to perform
the labelling, but this is both time-consuming and ex-
pensive. However, comparisons have not been car-
ried out on evaluating the difference in performance
of machine learning models when using automatic
versus human labelling. There has been very little
study done to support or recommend the best labelling
method. The lack of evaluation of these automatic la-
belling approaches motivates our work.
In this study, we have compared three state-of-
the-art automatic labelling methods with the inten-
tion of quickly producing sentiment labels for Twit-
ter data without human involvement. We have eval-
uated our approaches on two larger datasets from
the SemEval-13 and SemEval-16 competitions, which
contain ground truth and act as our human labelling.
In this paper, we will use the term ‘Human La-
belling’(HL) as ground truth throughout the discus-
sion. The labels: ‘Positive’, ‘Negative’, and ‘Neutral’
Biswas, S., Young, K. and Griffith, J.
A Comparison of Automatic Labelling Approaches for Sentiment Analysis.
DOI: 10.5220/0011265900003269
In Proceedings of the 11th International Conference on Data Science, Technology and Applications (DATA 2022), pages 312-319
ISBN: 978-989-758-583-8; ISSN: 2184-285X
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
are classified and assigned by the analysis of word
meanings and polarity scores according to the word
features and patterns. Three lexicon-based state-
of-the-art automatic labelling approaches: TextBlob,
Afinn, and Vader are used to generate all sentiment la-
bels. The results of automatic labelling are compared
to the results of human labelling using deep learning
algorithms for sentiment analysis in order to deter-
mine reasonable alternatives to labelling social media
data. The main objectives of our work are as follows:
Assign sentiment labels to the Twitter datasets
using three automatic labelling methods: Afinn,
TextBlob, and Vader.
To establish whether automatic labelling ap-
proaches would be a viable alternative as com-
pared with human labelling in terms of reducing
the time and expense of human labelling.
To obtain the best deep learning algorithm to anal-
yse sentiment when the Twitter datasets are la-
belled by three automatic labelling approaches.
The paper is organized as follows: in Section 2, re-
lated work on labelling strategies for state-of-the-art
classifier methods and sentiment analysis approaches
is presented. Section 3 describes our methodology,
while Section 4 details the experiment and discusses
the results. Finally, Section 5 concludes the paper
with a discussion of limitations and plans for future
Sentiment analysis is a challenging problem due to
the features of natural languages, such as the use
of words in different situations, indicating different
meanings. Sentiment analysis approaches are evalu-
ated on datasets with either human labels (Moham-
mad et al., 2016; Mohammad et al., 2017; Deriu
et al., 2016) or automatic labels (Saad et al., 2021;
Chakraborty et al., 2020). Human annotators assign
the sentiment labels for training and testing datasets
by their understanding and expertise. Most of the
datasets are annotated by human experts for rela-
tively small datasets. For example, (Deriu et al.,
2016; Mohammad et al., 2016) used human labelling
to predict sentiment. But, Oberlander and Klinger
(2018) found noisy labels (Sheng et al., 2008) in
the largest human annotated dataset (39k) (Crowd-
Flower, 2016). Several authors (Dimitrakakis and
Savu-Krohn, 2008; Lindstrom et al., 2010; Turney,
1994) studied data labelling costs and found that ob-
taining high-quality labelled data is time-consuming
and cost-ineffective. The alternative of obtaining sen-
timent labels using automatic labelling saves time and
money. The lexicon-based automatic labelling tech-
niques of TextBlob, Vader, and Afinn use the NLTK
(Natural Language Toolkit) of python libraries to au-
tomatically classify the sentiment of text and have
been used in many studies. Each lexicon-based sen-
timent labelling approach needs a predefined word
dictionary to infer the polarity of the sentiment ac-
cording to their rules. Vader dictionaries are used to
find the polarity scores of each word. Deepa et al.
(2019) assessed the polarity scores of words to cate-
gorize the sentiment for the Twitter dataset related to
UL Airlines with human labels using two dictionary-
based methods: Vader dictionaries and Sentibank;
the Vader dictionaries outperformed Sentibank by 3%
in their analysis to detect the polarity scores for the
sentiment classification using the Logistic Regres-
sion (LR) model. TextBlob and Afinn were used by
(Chakraborty et al., 2020) to label a large number
of tweets (226k) using ternary classes. They found
that TextBlob labelling generated normalized scales
of the sentiment labels compared to the Afinn la-
belling and obtained 81% accuracy in the LR clas-
sification method. Then, they proposed a fuzzy in-
ference rule that used the Vader dictionaries to as-
sign ternary sentiment labels and got 79% accuracy
in predicting the sentiment of sentences using the
Gaussian membership function. Saad et al. (2021)
used Afinn, TextBlob, and Vader to assign sentiment
labels to a drug review dataset, and they obtained
96% accuracy with TextBlob; similarly, (Hasan et al.,
2018) and (Wadera et al., 2020) also got success with
TextBlob labelling on Twitter datasets using some of
the traditional machine learning algorithms like Sup-
port Vector Machine (SVM), Na
ıve Bayes (NB), Ran-
dom Forest Tree (RFT), Decision Trees (DTC), and
K-Nearest Neighbours (KNN). There is no one auto-
matic labelling technique that does best consistently
in the previous work, and all the evaluations used ei-
ther human labelling or automatic labelling, but they
did not use both labelling strategies and did not com-
pare them.
State-of-art deep learning algorithms have been
used to predict the sentiment of text that is shared on
different social media in several studies (Poria et al.,
2015; Lei et al., 2016). Liao et al. (2017) proposed
a one-layer Convolution Neural Network (CNN) to
analyse the sentiment. Similarly, Long Short Term
Memory (LSTM), Bidirectional (BiLSTM) models
are also used in natural language processing (Xu et al.,
2019). However, all the above research used human
labelling in their state-of-the-art deep learning algo-
rithms, but they did not use automatic labelling.
A Comparison of Automatic Labelling Approaches for Sentiment Analysis
Figure 1: A summary of the experiments performed.
We have used two Twitter datasets from the SemEval-
13 and SemEval-16 competition which contains hu-
man labels (HL). We did not consider the remaining
datasets from this competition because they had ei-
ther imbalanced human labels or had a small number
of tweets. The details of the datasets are given in the
paper (Deriu et al., 2016) and (Yoon and Kim, 2017).
SemEval-2013 Task 4 and SemEval-2016 Task-4 are
named as DS-1 and DS-2 respectively in this study.
The original datasets are divided into train, dev, and
test. We combine the training, development, and test-
ing portions of each dataset to create a single whole
dataset. The first experiment uses the existing Se-
mEval labels (human labels) for both training and
testing. The second experiment uses, in turn, the
three automatic labelling methods (TextBlob, Vader,
and Afinn) for both the training and testing datasets.
The third experiment uses the existing SemEval labels
(human labels) for testing, but the training data labels
are generated using, in turn, the three automatic la-
belling methods (TextBlob, Vader, and Afinn). In all
cases, 80% of each dataset is used for training and the
remaining 20% of the dataset is used for testing. For
each experiment, CNN, BiSLTM, and CNN-BiSLTM
deep learning algorithms are applied to the cleaned
and pre-processed training data and the models are
tested on the test data. Figure 1 illustrates the details
of the methodology which is explained in the follow-
ing steps.
3.1 Data Labelling
In experiment 2 for both training and testing datasets
and in experiment 3 for only the training dataset, we
replace the human labels with automatically gener-
ated labels. Prior to assigning the automatic labels,
we remove the username and some special symbols
such as @, #, $, and RT from all tweets in the datasets.
We have used the Natural Language Toolkit (NLTK)
library from Python to label the tweet’s sentiment
as Positive, Negative or Neutral using the following
three methods:
TextBlob: In this study, we have used the polarity
of sentiment for textual data among the many proper-
ties available as part of TextBlob in the Python library.
TextBlob returns a polarity value within the range [-
1.0, 1.0] where ‘-1’ indicates a very Negative polarity,
‘0’ a Neutral polarity and ‘+1’ is a very Positive po-
Vader: VADER stands for ‘Valence Aware Dic-
tionary and sEntiment Reasoner’ (Hutto and Gilbert,
2014). It is a rule-based sentiment analysis tool,
which generates the scores of sentiments by the in-
tensity of lexical features and semantic meanings of
the text. Vader returns four components with associ-
ated intensity scores. For example, {neg: 0.106, neu:
0.769, pos: 0.125, compound: 0.1027}, where ‘neg’,
‘neu’, ‘pos’ indicate the Negative, Neutral, and Pos-
itive scores respectively and the compound score is
the normalized score of the summation of the valence
scores computed based on heuristic and lexicon senti-
ments. In this study, we assign ‘Positive’ label when
the compound score is greater than 0.05, ‘Negative’
label when the compound score is less than -0.05 and
otherwise, we assign ‘Neutral’ label.
Afinn: Afinn is a simple and popular lexicon de-
veloped by Finn Arup Nielsen (Nielsen, 2011). Afinn
returns the score of a word between [-5, 5]. Here we
assign a ‘Positive’ label when the score is greater than
‘0’, a ‘Negative’ label when the score is less than ‘0’
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
Table 1: Summary statistics of the total number of tweets from the SemEval competitions Tweet-2013 (DS-1) and Tweet-2016
(DS-2), and the percentage of Positive (Pos.), Negative (Neg.), and Neutral (Neut.) sentiment labels from human labelling
and automatic labelling approaches (TextBlob, Vader, Afinn).
Dataset Total
Human Labelling(%) Automatic Labelling(%)
TextBlob Vader Afinn
Pos Neg Neut Pos Neg Neut Pos Neg Neut Pos Neg Neut
DS-1 14885 38.23 15.84 45.94 48.66 19.55 31.41 51.55 18.2 29.99 45.76 17.96 36.01
DS-2 28631 38.41 15.67 45.93 46.34 20.28 32.4 47.06 24.48 27.48 42.1 22.87 34.05
and otherwise we assign a ‘Neutral’ label.
To illustrate the automatic labelling techniques we
take as an example from DS-2: “install the newest
version and you may change your mind!”; the sen-
timent label is assigned as ‘Neutral’, ‘Positive’, and
‘Positive’ using the TextBlob, Vader and Afinn meth-
ods respectively. It is noted that the existing human
(ground truth) label of the same text is also ‘Positive’.
Table 1 presents the two datasets with the original
name, the newly assigned name in our experiments,
and the total number of tweets associated with each
dataset. It also shows the summary of the percent-
age of sentiment labels per dataset with the automatic
labelling approaches in comparison to the human la-
bels. Positive labels are remarkably high when using
automatic labelling techniques. On the other hand,
Neutral labels are notably high in the human labelled
3.2 Data Preprocessing for Models
Data pre-processing and cleaning is an important step
when applying deep learning algorithms to the data.
We remove any unwanted words using a list of stop
words from the NLTK (Natural Language Toolkit) li-
brary in Python, without removing stop words like
‘our’, ‘ours’, ‘ourselves’, ‘these’, ‘those’ etc. We
removed all digits in the tweets and some unwanted
words which did not make any sense like ‘amp’, ‘tik-
tok’, ‘th’, etc., and inappropriate words. The cleaned
tweets are then tokenized after being split into train-
ing and testing sets. We have used GloVe of 300 di-
mensions for word embedding. GloVe, which stands
for Global Vectors, is an unsupervised learning algo-
rithm for achieving vector representations of words.
Large quantities of text are used for training and con-
verting into low dimensional and dense word forma-
tions in an unsupervised fashion by an embedding
process (Pennington et al., 2014). One hot encoding
is a highly popular technique to deal with categorical
data, which creates a binary column for each category
and returns a sparse matrix or dense array based on
the sparse parameter. We have labelled the sentiments
of tweets in three categories and used the one-hot en-
coding method. For this, three new columns are cre-
ated as ‘Positive’, ‘Negative’, and ‘Neutral’, and these
dummy variables are then filled up with Zeros (mean-
ing False) and ones (meaning True).
Figure 2: Structure of the deep learning models.
3.3 Deep Learning Models
This section describes the method of using deep learn-
ing models. CNN (Convolution Neural Network),
BiLSTM (Bidirectional Long Short-Term Memory),
and a combined model of CNN-BiLSTM are used to
find the sentiments of tweets. The model consists of
some common layers: an embedded layer, a Spatial-
Dropout1D layer, a deep learning layer, Dense layer,
Relu dense layer, Dense, and finally a Softmax layer
to predict sentiments. Three deep learning models:
CNN, BiLSTM, and CNN-BiLSTM are used sepa-
rately in the deep learning layer, which is in between
the SpatialDropout1D layer and the Dense layer of
the common layers. The architecture of the mod-
els is shown in Figure 2. A SpatialDropout1D layer
takes a word embedding matrix of an input sentence;
A Comparison of Automatic Labelling Approaches for Sentiment Analysis
It helps to prevent pixels from co-adapting with their
neighbors across feature maps, which promotes inde-
pendence between feature maps. The output is fed
to different deep learning networks. Each output of
the three deep learning models is fed separately into a
dense layer with a Relu activation function, and then
a final dense layer with a softmax activation function
predicts the probabilities of ternary sentiment labels.
CNN: A CNN is a feed-forward neural network
containing an input layer, hidden layer and an output
layer, which is capable of capturing all local features.
It computes the most important features from the out-
put of the CNN (Liao et al., 2017) with a Rectified
Linear Unit (ReLU) activation, and Global max pool-
ing layer.
BiLSTM: Long Short-Term Memory (LSTM) is a
Recurrent Neural Network (RNN) with three gates:
input gate, output gate, and forgot gate (Xu et al.,
2019). It has a forward and backward layer. BiLSTM
is capable of remembering future and past informa-
tion from input sequences and processing the infor-
mation in both directions.
CNN-BiLSTM: A CNN model is combined with
a BiLSTM (Xu et al., 2019) model. CNN-BiLSTM
includes all prominent level features and long-term
dependencies in both directions (Chaturvedi et al.,
2018) of input datasets for all labelling strategies in
the aforementioned three experiments.
3.4 Experiment Setup
Each deep learning model takes inputs from the Spa-
tialDropout1D layer which is embedded with se-
quence input. The maximum input sequence length
is 30. The CNN layer uses 64 filters with kernel
size 5. The BiLSTM layer is used with 64 hidden
units. A fully connected layer also is used with 64
hidden units. To avoid over fitting and under fit-
ting, CNN uses two dropout levels and BiLSTM and
CNN-BiLSTM models employ four dropout levels.
To get good results for different algorithms, we chose
varied drop out rates. For example, in the first ex-
periment, the drop out rates in the CNN model are
(0.2 and 0.2), however in the second and third ex-
periments, the drop out rates are (0.4, 0.5) and (0.3,
0.5), respectively. In the first, second, and third ex-
periments, the dropout rates for BiLSTM and CNN-
BiLSTM are (0.3,0.3,0.3,0.4), (0.2,0.2,0.2,0.5), and
(0.3,0.3,0.3,0.5), respectively. We have set a learn-
ing rate of 0.0001 for the first experiment (human la-
belling). A learning rate of 0.001 is considered for
the second and third experiments. Categorical cross-
entropy is used as the loss function and categorical
accuracy is used for the accuracy metric. We set the
batch size to be 100 and run for 10 epochs to aver-
age the metrics for accuracy, precision, F1 scores and
The performance of the models is measured by us-
ing four possible outcomes: True Positives (TP), True
Negatives (TN), False Positive (FP), and False Nega-
tive (FN). The accuracy of the result is computed us-
ing those outcomes, but accuracy can mislead in im-
balanced datasets. This is why, we have computed
precision, recall, and F1-score. Table 2 shows the re-
sults of the first experiment, which used three deep
learning models with human labelling for the two
datasets. The results are evaluated in macro averaged
performance across the Positive, Negative, and Neu-
tral classes. The BiLSTM model obtains the highest
accuracy, precision, F1-score and recall for the two
datasets. The best results are highlighted in bold in
the result Tables. The accuracy of the BiLSTM model
is 1.19% higher than the CNN model and 0.93%
higher than the CNN-BiLSTM model for DS-1. In
the same way, the accuracy of the BiLSTM model is
5.57 and 3.48% higher than the accuracy of the CNN
and CNN-BiLSTM model respectively for DS-2. The
F1-score is also higher with the BiSLTM model than
the other two models.
The results of the second experiment are shown in
Table 3 using the three automatic labelling strategies
and the three deep learning algorithms. The results
show higher accuracy, precision, recall, and F1-scores
for the two datasets with automatic labelling than hu-
man labelling. Additionally, Afinn labelling obtains
the highest accuracy using the BiLSTM model for
the two datasets compared to the other models with
TextBlob and Vader labelling techniques. The bal-
ance of sentiment labels are better in Afinn labelling
than the other labelling techniques, and this could ex-
plain why Afinn does better than the other labelling
strategies. On the other hand, the CNN model could
not learn all the dependencies of whole sentences
due to the limitation of filter lengths (Camg
u and
Kutlu, 2020). As a result, CNN and CNN-BiLSTM
models were not able to learn all features from sen-
tences or whole datasets. For this reason, BiLSTM
outperformed the other models with F1 scores of
77.54% and 80.7% for DS-1 and DS-2 respectively.
In the third experiment, we have observed the re-
sults with 80% of the training data with automatic la-
belling and 20% of test data with human labelling.
Table 4 displays the results of the third experiment
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
Table 2: Experiment 1: Comparison of Accuracy, Precision, F1-score and Recall with human labels using a CNN, BiLSTM,
and CNN-BiLSTM Model.
Model DS-1(%) DS-2(%)
Acc Pre F1 Rec Acc Pre F1 Rec
CNN+HL 60.36 65.50 55.19 52.08 54.97 66.92 56.39 50.48
BiLSTM+HL 61.55 71.95 58.25 54.25 60.54 66.79 61.99 57.99
CNN-BiLSTM+HL 60.62 63.82 55.97 53.99 57.06 66.14 57.74 52.93
Table 3: Experiment 2: Comparison of Accuracy, Precision, F1-Score and Recall with automatic labelling used for both
testing and training datasets using CNN, BiLSTM, and CNN-BiLSTM Models.
Model DS-1(%) DS-2(%)
Acc Pre F1 Rec Acc Pre F1 Rec
CNN+Af 74.26 76.51 72.24 69.37 74.28 77.48 74.39 71.67
BiLSTM+Af 80.17 81.63 77.54 74.92 80.05 84.06 80.70 77.80
CNN-BiLSTM+Af 75.27 78.11 73.04 69.79 76.9 81.16 77.9 74.93
CNN+Tb 63.17 73.70 61.37 55.60 69.27 76.09 69.69 65.8
BiLSTM+Tb 73.41 77.38 72.06 68.75 79.55 82.67 78.77 76.44
CNN-BiLSTM+Tb 66.95 72.77 65.00 61.15 75.03 78.46 74.5 71.92
CNN+Vd 68.22 72.8 66.17 62.37 68.19 76.09 71.04 67.03
BiLSTM+Vd 73.75 75.99 70.64 68.53 76.79 79.29 76.63 75.0 6
CNN-BiLSTM+Vd 71.12 73.46 66.96 63.81 72.4 76.58 73.56 70.89
Table 4: Experiment 3: Comparison of Accuracy, Precision, F1-score and Recall with automatic labelling techniques (training
dataset) and human labelling for the test set using CNN, BiLSTM, and CNN-BiLSTM Models.
Model DS-1(%) DS-2(%)
Acc Pre F1 Rec Acc Pre F1 Rec
CNN+Af 57.49 60.03 54.91 51.97 46.57 53.18 49.08 46.68
BiLSTM+Af 61.61 63.78 58.03 55.02 46.46 54.76 50.07 46.69
CNN-BiLSTM+Af 62.74 63.74 58.08 55.76 53.20 53.21 47.98 45.67
CNN+Tb 48.71 49.74 46.07 45.75 48.47 46.27 45.07 44.95
BiLSTM+Tb 55.91 63.79 52.67 48.52 49.47 50.27 45.07 44.95
CNN-BiLSTM+Tb 55.48 54.50 48.66 45.35 45.57 46.85 44.25 42.79
CNN+Vd 54.24 56.64 51.78 50.49 47.91 50.82 48.96 49.45
BiLSTM+Vd 55.32 60.29 53.19 50.65 48.24 51.08 48.6 50.33
CNN-BiLSTM+Vd 57.82 62.36 54.28 51.53 46.31 51.97 48.04 46.31
for the two datasets. The aim is to create a strenuous
test set to assess if the model can perform well when
tested with human labelling after being trained us-
ing automatic labelling. The accuracy, precision, F1-
score, and recall values for the CNN-BiLSTM model
were the best. These results are 2.12%, 2.29%, and
1.77% higher in accuracy, F1-score, and recall values,
respectively, than the results of the first experiment
for DS-1, though the precision value is 0.08% lower.
Similarly, the BiLSTM model obtained the highest
precision, F1-score, and recall, but the CNN-BiLSTM
model obtained the highest accuracy for DS-2. The
results are better in Experiment 3 than in Experiment
1. The model did best with Afinn labelling.
As can be seen with previous work (Chakraborty
et al., 2020; Saad et al., 2021; Deepa et al., 2019)
there is no one automatic labelling technique that does
best consistently. In our work, the Afinn labelling
technique almost always produces the best results,
across different models and experiments (Except in
one case, the best recall value is obtained for DS2 in
the Vader labelling technique). This labelling tech-
nique uses only sentiment of words (unlike Vader) and
may be better suited to Tweet data for this reason.
When considering the performance of the deep
learning models, the BiLSTM model does best in
most situations in Experiment 1 and Experiment 2. In
the third experiment, the CNN-BiLSTM model does
best with DS-1 but not with DS-2.
In Figure 3, ‘HL=Af’ represents the percentage of
sentiment labels that are the same across the human
labelling and Afinn labelling approaches. Similarly,
A Comparison of Automatic Labelling Approaches for Sentiment Analysis
Figure 3: Percentages of tweets that are labelled with the
same sentiment label by the human labellers and the auto-
matic techniques: Afinn, TextBlob and Vader.
‘HL=Tb’ and’ ‘HL=Vd’ represent the percentage of
labels that are the same across the human labelling
approach and the TextBlob labelling and Vader la-
belling respectively. The three automatic labelling
approaches: Afinn, TextBlob and Vader obtain an av-
erage of 59% and 50.55% of equal sentiment labels
with the human labelling for DS-1 and DS-2 respec-
tively. On the other hand, there is a much higher
overlap of equal sentiment labels between Vader and
Afinn (80%). We can also see in Figure 3 across the
different analyses and experiments that there is in-
consistency with the labels given by human labellers
and by the automatic labelling techniques with the
most agreement existing between Vader and Afinn
labelling techniques. In Figure 3, there is, at best,
a difference of 41% (and sometimes more) between
the human labels and automatic labels. This explains
why the performance results in Experiment 3 are of-
ten poorer than those reported in Experiment 1 and 2.
The learning task is much more difficult given that the
labels for the training data and the labels for the test
data are generated in different ways.
The best results are shown in Experiment 2 with
the automatic labelling approaches being used for
both the training and test sets. We argue that because
the same lexicon and rules have been used to label
both the training and test set it is easier for a ma-
chine learning approach to learn these rules and make
more accurate predictions. We suggest that this is not
the case with human labelling and hence the machine
learning models find it more difficult to do as well in
Experiment 1.
These experiments highlight the care that should be
taken in the use of both automatic and human la-
belling approaches. After evaluating both approaches,
we compared the performance of the three leading
automatic ternary labelling strategies to establish au-
tomatic labelling as a feasible alternative to human
labelling. This automated approach to sentiment la-
belling would yield extensive benefits by eliminat-
ing the effort and cost of human labelling. Our re-
sults have identified the Afinn approach as having the
highest level of labelling accuracy with both datasets.
We suggest that because both the training and test
sets were labelled with the same lexicon and rules,
a machine learning technique would find it easier to
learn these rules and produce more accurate predic-
tions. These experiments motivate the need for fur-
ther investigations into the differences between the
different automatic approaches as well as the differ-
ences between human labelling and the automatic la-
belling approaches. However, the limitations of this
study include the potential mislabelling of data using
automated approaches. We have evaluated the exper-
iments on a relatively small size of the dataset with
only three labelling approaches, and this proposed
methodology might not be suitable where the train-
ing dataset is smaller than the testing dataset. In the
future, we want to continue this experiment for other
large text-classification Twitter datasets, for example,
Covid19 or Covid-Vaccine related datasets. We will
also explore and extend this study to analyse the senti-
ment using other deep learning models with different
embedding methods, for example, BERT model with
word2vec and fast-text in the future. We hope these
studies would be helpful to analyse sentiment using
automatic labelling on large datasets when we want
to save time and cost for generating ground truth by
This work was supported by the College of Engineer-
ing, National University of Ireland, Galway.
u, Y. and Kutlu, Y. (2020). Analysis of fil-
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