Optimizing Natural Language Processing Applications for Sentiment

Analysis

Anderson Claiton Lopes

, Vitoria Zanon Gomes

and Geraldo Francisco Doneg

a Zafalon

Department of Computer Science and Statistics, Universidade Estadual Paulista (UNESP), Rua Crist

ao Colombo, 2265,

Jardim Nazareth, S

ao Jos

e do Rio Preto - SP, 15054-000, Brazil

Keywords:

Natural Language Processing, Sentiment Analysis, Machine Learning.

Abstract:

Recent technological advances have stimulated the exponential growth of social network data, driving an

increase in research into sentiment analysis. Thus, studies exploring the intersection of Natural Language

Processing and social network analysis are playing an important role, specially those one focused on heuristic

approaches and the integration of algorithms with machine learning. This work centers on the application

of sentiment analysis techniques, employing algorithms such as Logistic Regression and Support Vector Ma-

chines. The analyses were performed on datasets comprising 5,000 and 10,000 tweets, and our ﬁndings reveal

the efﬁcient performance of Logistic Regression in comparison with other approach. Logistc Regression im-

proved the performed in almost all measures, with emphasis to accuracy, recall and F1-Score.

1 INTRODUCTION

Natural Language Processing (NLP) is an area of Ar-

tiﬁcial Intelligence and Linguistics dedicated to mak-

ing computers understand statements or words written

in human languages. Natural language NLP emerged

to facilitate the user’s work and to satisfy the desire to

communicate with the computer in natural language

(Khurana et al., 2022).

To perform NLP consistently, it is necessary to

previously establish criteria to be followed, which

led to the use of algorithms and tools for this prob-

lem. The ﬁrst algorithm that stood out in history was

the Georgetown–IBM experiment, in 1954 (Hutchins,

2004). Between 1964 and 1966, (Weizenbaum, 1966)

developed the ELIZA program, considered the ﬁrst

chatbot. The big breakthrough in NLP came in the

1980s, thanks to advances in both hardware and soft-

ware with the use of Machine Learning (Bonaccorso,

2017).

Sentiment Analysis is an area of NLP that involves

support to computers to identify the sentiment behind

written content or analyzed audio. Adding this ability

to automatically detect sentiment in large volumes of

text and speech opens up new possibilities for algo-

https://orcid.org/0000-0003-2135-9947

https://orcid.org/0000-0003-4176-566X

https://orcid.org/0000-0003-2384-011X

rithm development (Ghosh and Gunning, 2019).

With the growth of Web 2.0 platforms such as

blogs, discussion forums, social networks and vari-

ous other types of media, consumers have within their

reach the power to share their experiences and opin-

ions, positive or negative, regarding any product or

service (Pang et al., 2008).

In the current literature it is possible to ﬁnd a vari-

ety of techniques used for sentiment analysis, such as

machine learning and lexicon-based approach (Med-

hat et al., 2014).

Although these techniques are widely used, such

as supervised and unsupervised learning, which use

algorithms such as Naive Bayes (Rish et al., 2001),

Bayesian Network (Kitson et al., 2021), Maximum

Entropy (Wu, 2012) and linear classiﬁers with the use

of Neural Networks (Abdi et al., 1999) and Support

Vector Machine (Mammone et al., 2009), these ap-

proaches suffer from the problem of correct polarity

classiﬁcation. This means that mistakes made in the

initial stages throughout the execution, inﬂuence the

ﬁnal quality of the result.

Obtaining better results in polarity classiﬁcation is

considerably important, considering the range of ac-

tivities that make use of sentiment analysis results.

Polarity concerns the classiﬁcation of a text if it is

a positive, negative or neutral citation. These activi-

ties carried out by companies and organizations bring

important returns to society, whether in the form of

698

Lopes, A., Gomes, V. and Zafalon, G.

Optimizing Natural Language Processing Applications for Sentiment Analysis.

DOI: 10.5220/0012632000003690

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 698-705

ISBN: 978-989-758-692-7; ISSN: 2184-4992

monitoring the reputation of companies, quality of

products and services, monitoring security and fraud

(Pang et al., 2002).

Thus, the aim of this work is to obtain better re-

sults in Natural Language Processing with Sentiment

Analysis in social networks, through the combination

of different techniques, carried out in the following

steps:

1. Linguistic pre-processing application for noise re-

duction, boosting the NLP processing action in or-

der to facilitate the classiﬁcation process.

2. Use of Machine Learning techniques with super-

vised learning and its main algorithms for data

classiﬁcation and sentiment analysis.

This work is organized as follows: in section 2, the

related works are presented; in section 3, is described

the development of the approach; in section 4, the ob-

tained results are presented and analyzed; ﬁnally, in

section 5, the conclusions are showed.

2 RELATED WORKS

In the literature, we ﬁnd several works, both in the

context of sentiment analysis using natural language

processing, and in sentiment analysis using machine

learning and deep learning.

2.1 Sentiment Analysis Using NLP

In (Qiu et al., 2010), a dictionary-based approach is

used to identify the sentiment of sentences in the con-

text of advertising. At work, an advertising strategy

was proposed to improve the relevance of ads and the

user experience. The authors worked with data from

web forums. The results showed that the proposed

model performed well in advertising keyword extrac-

tion and ad selection.

In (Hatzivassiloglou and McKeown, 1997), an ini-

tial list of opinion adjectives was used along with a

set of linguistic restrictions to identify more opinion

words and their orientations.

2.2 Sentiment Analysis Using Machine

Learning

In (Kang et al., 2012), an optimized Naive Bayes clas-

siﬁer is used to solve the trend problem of greater

accuracy in classifying positive samples (about 10%

higher than in negative samples). This creates the

problem of decreasing mean accuracy when the ac-

curacy for the two classes is counted together. The

work showed that using this algorithm together with

a database of restaurant reviews it was possible to re-

duce the difference in accuracy between classes when

compared to the traditional Naive Bayes and the Sup-

port Vector Machine (SVM). Recall and precision

measures also improved.

In (Chen and Tseng, 2011), two SVM-based

multiclass algorithms are used: One-against-All and

Single-Machine Multi SVM to categorize comments.

They proposed a method to assess the quality of infor-

mation in analyzed products considering it as a classi-

ﬁcation problem. They also used an information qual-

ity framework to ﬁnd the set of information-oriented

attributes. They worked on reviews of digital cameras

and MP3 players. The results showed that the method

can correctly classify reviews in terms of its quality

and that it signiﬁcantly outperforms advanced meth-

ods.

The unsupervised approach was also used in (Xi-

anghua et al., 2013) to automatically detect the as-

pects discussed in Chinese social networks and also

the feelings expressed in different aspects. An LDA

(Latent Dirichlet Allocation) model was used to un-

cover multi-aspect global themes from social com-

mentary, then researchers extracted local theme and

associated sentiment based on a scrolling window

context over the text. They worked on social com-

ments that were extracted from a blog dataset (2000-

SINA) and a lexicon (300-SINA HowNet).

They also showed that their approach achieved

good results in separating themes and improved the

accuracy of sentiment analysis. The model also

helped uncover several aspects of the topics and as-

sociated sentiment. (Ko and Seo, 2000) proposed

a method that divides documents into sentences and

classiﬁes each sentence using the keyword lists of

each category and a measure of sentence similarity.

In (Zharmagambetov and Pak, 2015), sets of de-

cision trees are used to perform sentiment analysis

on movie reviews. However, for extracting attributes

from the text, they used deep learning methods. The

methodology chosen was Word2Vec, which allows

the capture of semantic characteristics of words. The

vectors obtained by the feature extraction process

were clustered by k-means. Each cluster, out of a to-

tal of 2000, had an average of 5 words. These were

selected by their proximity in vector space. Clusters

were used as inputs to the classiﬁer. In the work, a ref-

erence database with movie reviews was used. The re-

sult of the experiment showed that the approach using

feature extraction by deep learning was signiﬁcantly

better than the one using bag-of-words with 5000 en-

tries.

Optimizing Natural Language Processing Applications for Sentiment Analysis

699

2.3 Sentiment Analysis Using Deep

Learning

Finally, deep neural networks were used in (Hu et al.,

2015) for the analysis of sentiment in reviews of elec-

tronic products, movies and hotels. The authors cre-

ated a classiﬁcation framework that uses 3 differ-

ent methods for attribute extraction: frequency-based,

context-based, and part-of-speech tagging. Each

method feeds a neural subnet that reduces the dimen-

sionality of the attribute space. The outputs of these

subnetworks feed the main network that is responsible

for the analysis of feeling.

3 DEVELOPMENT

3.1 Data Collect

The collected data comprises a collection of tweets,

each labeled with the sentiment expressed in the

tweet, which can be positive, negative, or neutral.

This dataset contains a diverse range of tweets, cap-

turing opinions, emotions, and attitudes of Twitter

users regarding various topics such as movies, prod-

ucts, events, or general experiences.

The utilized dataset consists of exactly 5,000 pos-

itive tweets and 5,000 negative tweets. The exact bal-

ance between these classes is not a coincidence; the

intention is to maintain a balanced dataset.

3.2 Data Preparation

Data preprocessing aims to enhance the quality and

effectiveness of the analyses and models that will be

applied to the data.

Concatenating lists of data in Natural Language

Processing (NLP) is a recommended technique in cer-

tain scenarios as it can improve data representation

and provide additional insights for analysis or mod-

eling. This technique is particularly useful when text

data comes from various sources or different contexts

and one wants to create a single, more diverse and

comprehensive dataset.

In machine learning, including applications of

Natural Language Processing (NLP) and Artiﬁcial In-

telligence (AI), splitting the dataset into training and

testing sets is a common and crucial practice.

In this speciﬁc case, we have chosen a 20% por-

tion for testing and 80% for training, considering that

the test set is relatively large, which can be beneﬁcial

when dealing with a sufﬁciently large dataset.

3.3 Data Preprocessing

Data preprocessing is one of the critical steps in any

machine learning project. It involves cleaning and

formatting the data before feeding it into a machine

learning algorithm.

In this project, we utilized the following tasks:

• Tokenization. Tokenization involves dividing

strings into individual words without white spaces

or tabs.

• Lowercasing. In this step, we also converted each

word in the string to lowercase.

• Removal of Stopwords and Punctuation. As we

are working with Twitter data, we removed some

commonly used substrings on the platform, such

as hashtags, retweet tags, and hyperlinks. For this,

we utilized the ”re” library to perform regular ex-

pression operations on our tweets. We deﬁned a

search pattern using the sub() method to remove

matches and replace them with an empty charac-

ter.

• Stemming. This process involves converting a

word to its most general form or root. It helps

to reduce the size of our vocabulary. For exam-

ple, words like ”learn,” ”learning,” and ”learned”

all derive from their common root ”learn.” How-

ever, in some cases, the stemming process pro-

duces words that are not correct spellings of the

root word.

Since we are using the NLTK library, we have ac-

cess to various modules for stemming. In this ker-

nel, we used the Porter Stemmer. The objective of

using this algorithm is to simplify words to their

root form heuristically, meaning by following speciﬁc

rules without relying on a complete dictionary.

Mapping each word-sentiment pair and its fre-

quency is a crucial step in text processing for senti-

ment analysis tasks. This approach is commonly em-

ployed to represent text in a format suitable for apply-

ing machine learning algorithms or other text analysis

techniques.

Moreover, it is necessary to extract features from

the preprocessed data, such as word frequency and n-

grams. These features will be used to train and test

the machine learning models.

The Word Count Table, also known as a word his-

togram, is a tabular representation that displays the

frequency of word occurrences in a text or set of texts.

Through this table, we will conduct a Descriptive

Analysis, obtaining a descriptive view of the distribu-

tion of words found in the tweets.

Thus, it is necessary to select a speciﬁc set of

words for visualization. This process is recommended

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for text analysis and natural language processing situ-

ations and is often referred to as ”Keyword Analysis.”

It can provide valuable insights into the content, top-

ics, sentiments, or speciﬁc characteristics of the text.

Figure 1 illustrates the Python commands per-

formed for Word Selection for Visualization.

Figure 1: Visualizing the words of Python commands.

Figure 2: Python Commands for Word Selection for Visu-

alization.

As described, there is a focus on relevant informa-

tion. Some words such as ”good,” ”song,” and ”play”

appear in both positive and negative word counts, in-

dicating a dual sense of the word. Other words like

”happy,” ”nice,” and ”sad” stand out in their respec-

tive polarities, and the substantial count of punctua-

tions and emojis, which depending on context, can be

positive, negative, or neutral.

To provide a better illustration, let’s use a scatter

plot to visually inspect this table.

We have 3568 counts in the positive area, while

only 2 in the negative area. The red line marks

the boundary between positive and negative regions.

Words close to the red line might be classiﬁed as neu-

tral. Figure 3 illustrates the Visualization of the Scat-

ter Plot.

Figure 3: Scatter Plot.

According to (Bisong and Bisong, 2019), a fre-

quency dictionary is a crucial tool in sentiment anal-

ysis, especially when using logistic regression as a

classiﬁcation method. It’s used to capture informa-

tion about the frequency of words in relation to spe-

ciﬁc sentiments (positive, negative, neutral) present in

the training data.

Now we need to process the tweets, tokenizing

them into individual words, removing stopwords, and

applying stemming. For this purpose, we use the

function process tweet( ).

3.4 Deﬁnition of Algorithm and Model

Deﬁning an algorithm and model is a fundamental

step to achieve accurate and reliable results. In this

article, we have chosen the Logistic Regression algo-

rithm to train and evaluate the results.

The choice of Logistic Regression was due to its

ease of interpretability, which is particularly useful

for understanding the importance of each variable.

Additionally, its simplicity makes it quick to train and

comprehend.

The model deﬁnition is represented by equations

1 and 2:

h(z) =

1 + exp

−z

(1)

z = θ

+ θ

+ ...θ

(2)

Within the model, it is necessary to deﬁne the Cost

and Gradient Function (equation 3), which is the av-

erage of the logarithmic loss across all training exam-

ples. This is considered a fundamental step in opti-

mizing logistic regression models, as well as in many

other machine learning algorithms. It plays a crucial

role in the model training step, where the objective is

Optimizing Natural Language Processing Applications for Sentiment Analysis

701

to adjust the model’s parameters to minimize the error

and enhance prediction performance.

J(θ) = −

∑

i=1

(i)

log(h(z(θ)

(i)

))

+ (1 − y

(i)

)log(1 − h(z(θ)

(i)

))

(3)

Where: - m is the number of training examples.

- y

(i)

is the actual label of the i-th training exam-

ple.

- h(z(θ)

(i)

) is the model’s prediction for the i-th

training example.

In addition to the Cost and Gradient Function, it is

necessary to deﬁne the loss function for a single train-

ing example. The goal is to measure the discrepancy

between the predictions made by the model and the

actual (label) values of the training data. This can be

veriﬁed by equation 4:

Loss = −1 ×



(i)

log(h(z(θ)

(i)

))

+(1 − y

(i)

)log(1 − h(z(θ)

(i)

))



(4)

Note that when the model predicts 1 (h(z(θ)) =

1) and the label y is also 1, the loss for that training

example is 0. Similarly, when the model predicts 0

(h(z(θ)) = 0) and the actual label is also 0, the loss

for that training example will be 0.

Another important step is updating the weights

during the training of machine learning models. To

update the weight vector θ, we apply gradient descent

to iteratively improve the model’s predictions.

The gradient of the cost function J with respect

to one of the weights θ

is, which can be veriﬁed by

equation 5:

∇

J(θ) =

∑

i=1

(i)

− y

(i)

(5)

- i is the index over all m training examples.

- j is the index of weight θ

, where x

is the feature

associated with weight θ

To update the weight θ

, we adjust it by subtract-

ing a fraction of the gradient determined by α, using

equation 6:

= θ

− α × ∇

J(θ) (6)

The learning rate α is a value we choose to control

the size of a single update.

3.5 Feature Extraction

Feature extraction involves identifying and select-

ing the most relevant and informative features or at-

tributes from the original data to be used as inputs in

the model.

We have a list of tweets that we need to extract

and store in a matrix. The ﬁrst extracted feature is the

number of positive words in a tweet, and the second

feature is the number of negative words in a tweet.

3.6 Model Training

Model training is essential in machine learning and

sentiment analysis. It teaches the model to recognize

patterns in data, adjust parameters for accurate pre-

dictions, and generalize to new examples. This leads

to better automated decisions, insights into data rela-

tionships, and performance optimization. The itera-

tive training process adapts the model to the speciﬁc

problem and allows continuous updates to maintain

its relevance over time.

After feature extraction, we initiate the training

process by stacking the features for all training ex-

amples into a matrix X.

3.7 Results Visualization

According to (Bisong and Bisong, 2019), visualiza-

tion can aid in the selection of relevant features.

By plotting scatter plots, correlation matrices, or bar

charts, you can identify which features have the most

inﬂuence on the outcomes and decide which ones to

include in the model.

After stacking, the next step is to visualize the

tweets and see how they are distributed along the X

and Y axes. Figure 4 presents the Python commands

for visualizing the samples.

Figure 4: Python commands for visualizing the samples.

3.8 Model Evaluation

According to (Bisong and Bisong, 2019), evaluating

the model is crucial to measure its ability to make

accurate predictions on unseen data. This veriﬁes

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whether the model generalizes well, avoiding over-

ﬁtting. Evaluation reveals the actual performance of

the model, aiding in adjusting hyperparameters and

choosing the best path. It also helps identify issues

like bias or systematic errors, providing insights for

improvements and validating the practical utility of

the model.

4 EVALUATION AND RESULTS

In this section, the testing methodology employed and

the results obtained from the execution of the pro-

posed method in this study are discussed. The perfor-

mance of the algorithms is compared using evaluation

metrics.

The evaluation metrics utilized include:

• Accuracy. To measure the proportion of correct

predictions in relation to the total number of pre-

dictions made by the algorithm.

• Precision. To assess the proportion of true pos-

itives in relation to the total positive predictions

made by the algorithm.

• Recall. To measure the model’s ability to identify

all positive examples in a dataset.

• F1 Score. To measure the harmonic mean be-

tween precision and recall.

4.1 Testing Platform

The platform used was Google Colab, along with

Jupyter Notebook, including Python version 3.7 and

all necessary packages, with the main library being

NLTK.

The choice of Google Colab is due to its cloud-

based nature, minimizing the time spent on setting

up development environments and acquiring NLP li-

braries, thus expediting the project’s initiation.

Moreover, Colab allows for the utilization of com-

putational resources from the Google Cloud Platform,

including access to GPUs and TPUs, which can sig-

niﬁcantly accelerate the training of NLP models, es-

pecially deep learning models. Colab employs inter-

active Jupyter notebooks, enabling the writing and ex-

ecution of code in blocks.

4.2 Logistic Regression vs SVM

Approach

In the applied testing methodology, two distinct test

cases were conducted, involving variations in the

Table 1: Comparison of Performance: Logistic Regression

vs. SVM - 5,000 Tweets.

Metric Logistic Regression SVM

Accuracy 0.835 0.827

Precision 0.858 0.826

Recall 0.952 0.993

F1-Score 0.903 0.902

Table 2: Comparison of Performance: Logistic Regression

vs. SVM - 10,000 Tweets.

Metric Logistic Regression SVM

Accuracy 0.773 0.767

Precision 0.795 0.811

Recall 0.740 0.700

F1-Score 0.766 0.751

Figure 5: Performance Comparison (5.000 tweets).

Figure 6: Performance Comparison (10.000 tweets).

Optimizing Natural Language Processing Applications for Sentiment Analysis

703

number of words. Consequently, each test case under-

went four iterations to derive metrics encompassing

accuracy, precision, recall, and F1-Score. Initially,

the input number of words was ﬁxed at 5,000. Subse-

quently, this parameter was augmented to 10,000.

The outcomes of these method executions are pre-

sented in Tables 1 and 2.

Observations reveal that in the test case involving

5,000 words, the SVM approach produced subopti-

mal performance results. Nevertheless, upon imple-

menting the strategy of increasing the word count to

10,000, only slight variations in the outcomes were

noted.

Conversely, the Logistic Regression approach ex-

hibited robust consistency, with results remaining

largely unaffected despite the variance in word count.

Therefore, the signiﬁcance of the Logistic Re-

gression algorithm is underscored in methodologies

where the dataset encompasses a larger number of

words and demonstrates greater variation.

Therefore, Logistic Regression stands out as the

number of words increases.

5 CONCLUSIONS

In conclusion, the importance of employing Super-

vised Machine Learning for Sentiment Analysis is un-

derscored, providing a robust framework that delivers

satisfactory results, particularly when handling exten-

sive datasets.

Nevertheless, it is noteworthy that the ﬁeld lacks

a standardized computational method for Sentiment

Analysis, resulting in outcome variations contingent

on the speciﬁc techniques, algorithms, and models

employed.

The utilization of algorithms such as Logistic Re-

gression and SVM has proven instrumental in pro-

cessing large volumes of textual data, markedly aug-

menting computational capabilities for such tasks.

The outcomes of this study exhibit a level of perfor-

mance comparable to published works in the ﬁeld,

signifying the efﬁcacy of the proposed approach.

In essence, this method stands poised to aid pro-

fessionals in the realm of computational intelligence

engaged in Sentiment Analysis studies, offering a

well-suited avenue for discerning polarities among

analyzed words irrespective of the data source.

Finally, as future work, we will implement new

machine learning method and compare them with the

present ones. Moreover, we intend to establish com-

parisons with other approaches proposed in the litera-

ture, concerning the sentiment analysis issue.

ACKNOWLEDGEMENTS

The authors would like to thank Coordenac¸

ao de

Aperfeic¸oamento de Pessoal de N

ıvel Superior -

Brasil (CAPES), under grant 88887.686064/2022-00,

and S

ao Paulo Research Foundation (FAPESP), under

grant 2020/08615-8, for the partial ﬁnancial support.

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