A Comprehensive Survey on Sentiment Analysis Techniques
Corresponding email: tkneo@mmu.edu.my
Published at : 31 Oct 2023
Volume : IJtech
Vol 14, No 6 (2023)
DOI : https://doi.org/10.14716/ijtech.v14i6.6632
Aftab, F., Bazai, S.U., Marjan, S., Baloch, L., Aslam, S., Amphawan, A., Neo, T.-K., 2023. A Comprehensive Survey on Sentiment Analysis Techniques. International Journal of Technology. Volume 14(6), pp. 1288-1298
| Farhan Aftab | Department of Computer Engineering, Balochistan University of Information, Technology, Engineering and Management Sciences, Quetta 87300, Pakistan |
| Sibghat Ullah Bazai | Department of Computer Engineering, Balochistan University of Information, Technology, Engineering and Management Sciences, Quetta 87300, Pakistan |
| Shah Marjan | Department of Software Engineering, Balochistan University of Information, Technology, Engineering and Management Sciences, Quetta 87300, Pakistan |
| Laila Baloch | Department of Computer Engineering, Balochistan University of Information, Technology, Engineering and Management Sciences, Quetta 87300, Pakistan |
| Saad Aslam | Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Selangor 47500, Malaysia |
| Angela Amphawan | Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Selangor 47500, Malaysia |
| Tse Kian Neo | CAMELOT Faculty of Creative Multimedia, Multimedia University, Cyberjaya 63100, Selangor, Malaysia |
Sentiment analysis is a natural language processing (NLP)
technique used to decide if the underlying sentiment is positive, negative, or
neutral. Subjective information from the text can be extracted using sentiment
analysis by recognizing its context and position. Data from a variety of
sources, like social network comments, news stories, consumer reviews, and
more, can be used for sentiment analysis. Sentiment analysis uses different
algorithms to analyze words, phrases, and context available in text and different
procedures to determine the overall sentiment communicated. There are various
ways in which sentiment analysis is performed, ranging from rule-based methods
that use lists of positive and negative terms as labeled data for training
machine learning algorithms to building classifiers. Understanding social
sentiment, underlying intents, and responses to various characteristics of
humans can be done with the help of sentiment analysis, which helps in
decision-making. The primary goal of this work is to provide the audience with
the knowledge needed to understand sentiment analysis, highlight potential
opportunities and challenges, and investigate recent studies that have been
published in reputable resources focusing on the field of sentiment analysis in
NLP.
Convolutional Neural Network (CNN); Internet Movie Database (IMDb); Machine Learning; Recurrent Neural Network (RNN); Sentiment Analysis
Sentiment analysis, often known as opinion mining, looks at how individuals feel about particular things. Sentiment analysis is a subfield of computational linguistics and NLP that deals with techniques for extracting, categorizing, comprehending, and assessing the opinions expressed in online publications (Siswanto et al., 2022). It is also known as opinion mining of texts (Techtarget, 2023). The advancement of sentiment analysis has been made easier by the abundance of online text data, especially when it comes to speculating on people's attitudes, opinions, and beliefs. Sentiment analysis has been widely utilized to forecast public sentiment and trends in a variety of scenarios. Academics studying political communication use sentiment analysis on social media posts to gather public opinion on presidential candidates and to precisely forecast election outcome.
Studies in
economics show that stock market trends can be predicted by sentiment analysis
of news reports, social media posts, and other sources of information (Ren, Wu, and Liu, 2018). Sentiment analysis has grown in
importance as a component of natural language processing. Sentiment analysis in
teaching assessment can assist teachers in precisely and promptly modifying the
lesson plan to reflect students' genuine feelings about the course to raise the
Caliber of instruction (Zhai et
al., 2020).
Online commerce is one of the economic areas in the contemporary world that is expanding the quickest. Nowadays, a lot of goods are purchased through online merchants. Reviews frequently affect online product purchases. Therefore, the importance of finding fake reviews is increasing, and the success of a system for identifying bogus reviews relies heavily on sentiment analysis (Hassan and Islam, 2021).
Figure 1 Sentiment Analysis Methods
Figure 1 illustrates the important approaches used for Sentiment analysis, like deep learning approaches such as CNN and RNN, which can extract complex patterns from text. We present an overview of the current state-of-the-art techniques in sentiment analysis in this survey study, including the primary methodologies, applications, datasets, evaluation criteria, and problems. We also examine current trends and future directions in the discipline, highlighting growing research topics and potential prospects. Overall, the purpose of this survey article is to give a complete and up-to-date review of the topic of sentiment analysis, which can be used by both scholars and practitioners.
Applications
Sentiment
analysis has a wide range of applications across various fields, including:
1. Customer
insights: By utilizing sentiment analysis, businesses can gain insights into
their customers' attitudes, perceptions, and sentiments regarding their
products, services, or brand. Making data-driven decisions to enhance their
offerings and improve the customer experience can assist organizations in
gaining a deeper understanding of their customers' needs, preferences, and pain
points
2. Brand
management: It's crucial for a company to grasp the factors driving customer
emotions, what aspects of its product or service succeed, and what doesn't.
Machine learning, facilitated by sentiment mining techniques, empowers
businesses to comprehend their customers' sentiments towards the brand and,
most importantly, their expectations. Even when feedback is conveyed through
videos, Repustate's sentiment analysis tool, equipped with its Video Content
Analysis (VCA) feature, ensures that no vital data is missed. These data can be
used by businesses to proactively neutralize negative feelings and develop a
more focused branding strategy (Robinson,
2021; Trends, 2020).
3. Decision-making
and strategy development: Sentiment analysis provides valuable data for decision-making
and strategy development. By understanding customer sentiment and feedback,
businesses can make informed decisions about product improvements, marketing
strategies, customer engagement, and other business initiatives (Repustate, 2022).
3. Literature
The findings
showed that various machine learning algorithms produced varying degrees of
accuracy; Naive Bayes and Support Vector Machines are among the top techniques.
Pretrained embeddings were employed in various studies, along with deep
learning techniques like CNNs and RNNs. Deep learning models for sentiment
analysis, including CNNs, RNNs, LSTMs, and GRUs, have gained popularity. Various
machine learning algorithms and deep learning approaches were employed, with
some models reaching great accuracy on sentiment analysis datasets such as the
IMDb, Twitter US Airline Sentiment, and Sentiment140. However, there are still
difficulties in effectively analyzing poorly organized and caustic words, as
well as a lack of fine-grained sentiment analysis, reliance on annotated data,
and potential bias in training data. The research emphasizes the shortcomings of
present sentiment analysis approaches and recommends that more trustworthy
language models are required to solve the obstacles provided by poorly
organized and sarcastic messages (Tan, Lee, and Lim,
2023).
This scientific research examines
numerous strategies for sentiment analysis in textual data, such as product and
consumer evaluations, social media posts, and other types of data. It outlines
two fundamental methodologies: lexicon-based techniques and machine-learning
approaches. The Lexicon-Based Approach employs a sentiment lexicon, which
contains information on positive and negative words and phrases. The research
investigates several machine learning methods used in sentiment analysis, such
as Naive Bayes, linear regression, SVM, and deep learning. The researchers
believe sentiment analysis may be used to aid decision-making in numerous
sectors of the economy (Appiahene
et al., 2022).
The research covers a thorough
evaluation of the literature to find the most effective machine learning-based
approaches for doing Urdu-based sentiment analysis (SA). The evaluation sought
to uncover primary papers addressing machine learning-based SA concerns
published in the last four years. 40 papers were chosen and evaluated using
quality evaluation indicators. According to the findings, machine learning
approaches, including deep learning and supervised learning, have been widely
applied to Urdu-based SA. To enhance the overall performance of
sentiment analysis (SA), the research suggests combining SA techniques with
information retrieval, machine translation, and natural language processing
(NLP) approaches (Liaqat et al., 2022).
The paper provides an overview of
the issues and approaches associated with sentiment analysis, often known as
opinion mining, which uses NLP to extract relevant information from internet
resources. This is critical for businesses and government agencies seeking
accurate user feedback for future actions. They show how combining machine
learning and dictionary-based techniques may improve sentiment categorization
accuracy dramatically. The value of sentiment analysis in providing
decision-making information is emphasized in applications of sentiment analysis
within the industry and academic research. Nonetheless, several obstacles
persist (Gouthamia and Hegde, 2021).
This research presents the
findings of a comprehensive review of 18 research studies on sentiment analysis
in NLP. For sentiment analysis, the research used machine learning algorithms,
with Nave Bayes being the most used. Datasets were frequently culled from
microblogs like Twitter and other internet sources. Lexicon-based approaches
were also utilized to extract characteristics such as unigrams, enhanced words,
and bigrams from Turkish, Arabic, and Bengali texts, with accuracies ranging
from 73% (Hilario et al., 2021).
In this paper (Hassan and Islam, 2021), TF-IDF-based sentiment
classification model was developed that can classify sentiment value with 92%
accuracy. In this study (Goel and Batra, 2020),
Sentiment Analysis was carried out using a deep neural network called the RNN
as well as machine learning methods, and this research discovered that the RNN
model performed more accurately. This study (Poornima
and Priya, 2020) examined the performance of three machine learning
methods: SVM, Multinomial Naive Bayes, and Logistic regression. When the Bigram
model was utilized, the Logistic Regression attained an accuracy of roughly
86%.
The research presents an
introduction to the difficulties and methodologies involved in sentiment
analysis. The authors discuss big data sets, sentiment analysis on non-textual
material, and the importance of accuracy, precision, recall, and the F-measure
in assessing results. They demonstrate how combining machine learning and
dictionary-based approaches may significantly enhance sentiment classification
accuracy. The importance of sentiment analysis in delivering decision-making
information is underlined in sentiment analysis applications in industry and
academic research. Nonetheless, several challenges remain (Hamed, Ezzat, and Hefny, 2020).
This scholarly research delves
into the field of sentiment analysis, also known as opinion mining, and how it
can detect and categorize emotions and views conveyed in the text. The research
investigates many ways of sentiment analysis, such as machine learning and
lexical analysis. It emphasizes the importance of proper training sets for
accurate sentiment analysis as well as linguistic components of natural
language processing. The paper concludes by examining the potential
applications and benefits of sentiment analysis. It examines several sentiment
analysis machine learning techniques, such as Naive Bayes, Maximum Entropy, and
Support Vector Machines. According to the study, SVM, Naive Bayes, and neural
networks have the best accuracy and may be regarded as baseline learning
techniques. However, lexicon-based strategies were also beneficial in other
cases. The study emphasizes the significance of gathering large volumes of data
for sentiment analysis to provide appropriate findings (Mehta
and Pandya, 2020).
This scientific paper explores
numerous machine learning algorithms used for sentiment analysis, specifically
in social media. The research dives into many levels of sentiment analysis,
such as document, sentence, aspect, phrase, and feature level. The authors also
address how sentiment analysis may be applied to specific topics, such as
analyzing tweets about Alzheimer's illness and spotting email spam using
personality identification. The authors examine the performance of several
machine learning algorithms, such as Naive Bayes, Random Forest, and SVM, in
sentiment analysis. Random forest method using Unigram with Sentiwordnet
includes negation words, for example, achieves the greatest accuracy of 95.6%.
Overall, this scholarly research gives great insight into the numerous
machine-learning approaches utilized in social media sentiment analysis and
their applicability in diverse fields (Bhatt and
Swarndeep, 2020).
This paper presents a word vector
refinement model based on an improved genetic algorithm, which employs improved
genetic algorithms to obtain the optimized word vector such that it can be
closer to a set of semantically and emotionally similar nearest neighbors and
further away from emotionally dissimilar neighbors. Additionally, the model
employs a sentiment lexicon to obtain the sentiment ranking of semantic nearest
neighbors (Li and Liang, 2020).
This article examines the methodology, platforms, and applications of sentiment analysis in social media from 2014 to 2019, focusing on global events, healthcare, politics, and business. The two main methodologies identified are a lexicon-based approach and a machine learning approach, with most research collecting data on Twitter as the principal platform. Sentiment analysis has applications ranging from disaster response and recovery to detecting security breaches, recognizing sentiment demands during crises, and estimating depression levels. The study addresses prominent sites for information extraction, its extensive uses, and how sentiment analysis is used in many areas (Drus and Khalid, 2019).
Figure 2 Categorization of a number of papers based on different categories.
Figure 2 categorizes research papers into four key areas: Methodologies (categorized into Lexicon-Based, Machine Learning, and Hybrid Approaches), Applications (covering various domains like social media, Healthcare (Artera, 2021), Politics, Business, and Disaster Response), and Performance and Evaluation (including discussions on Accuracy and Performance, as well as Challenges and Limitations). Additionally, it classifies papers based on their data sources, primarily focusing on the use of Twitter data and other sources
Table 1 Summary of Research Papers on Sentiment Analysis
Techniques
|
Paper
Title |
Focus |
Methodologies |
Findings/Key
Points |
|
Machine
Learning Approaches for Sentiment Analysis (Tan,
Lee, and Lim, 2023) |
Machine
Learning Approaches in Sentiment Analysis |
Various
machine learning algorithms, Deep learning models |
Comparison
of machine learning algorithms and deep learning approaches in sentiment
analysis. Challenges in analyzing poorly organized and caustic words, lack of
fine-grained sentiment analysis, and potential bias in training data.
Importance of more trustworthy language models. |
|
Strategies
for Sentiment Analysis in Textual Data (Appiahene
et al., 2022) |
Methodologies for Sentiment
Analysis |
Lexicon-based techniques,
Machine learning methods |
Sentiment analysis is used to
analyze attitudes on LGBTQ+ issues. Positive sentiments outweigh negative
thoughts. Importance of sentiment analysis in decision-making. |
|
Machine
Learning-based Approaches for Urdu-based Sentiment Analysis (Liaqat et al., 2022) |
Machine
Learning Approaches for Urdu-based Sentiment Analysis |
Machine
learning and deep learning approaches, Combination with other techniques |
Application
of machine learning and deep learning approaches in Urdu-based sentiment
analysis. Importance of combining sentiment analysis techniques with other
approaches. |
|
Challenges
and Approaches in Sentiment Analysis (Gouthamia and
Hegde, 2021) |
Issues and Approaches in
Sentiment Analysis |
Challenges in sentiment
analysis, Accuracy, and evaluation metrics |
Difficulties in determining
precise sentiment meaning and polarity. Importance of accuracy, precision,
recall, and F-measure in evaluating results. A combination of machine
learning and dictionary-based techniques can improve accuracy. |
|
TF-IDF
based Sentiment Classification Model (Hassan and
Islam, 2021) |
TF-IDF
based Sentiment Classification |
TF-IDF,
Sentiment classification model |
Development
of a sentiment classification model based on TF-IDF with 92% accuracy. |
|
Difficulties
and Methodologies in Sentiment Analysis (Hamed,
Ezzat, and Hefny, 2020) |
Difficulties and Methodologies
in Sentiment Analysis |
Challenges in sentiment
analysis, Combination of machine learning and dictionary-based approaches |
Challenges in sentiment
analysis, such as sentiment polarity recognition. Importance of sentiment
analysis in decision-making. |
|
Improved
Genetic Algorithm for Word Vector Refinement in Sentiment Analysis (Li and Liang, 2020) |
Word
Vector Refinement Model using Genetic Algorithm |
Improved
Genetic Algorithm, Sentiment lexicon |
Use of
improved genetic algorithm for word vector refinement in sentiment analysis.
Importance of sentiment lexicon for sentiment ranking. |
|
Short-Text
Sentiment Analysis using CNN-BiLSTM (Yue and Li, 2020) |
Short-Text Sentiment Analysis
using CNN-BiLSTM |
CNN, Bidirectional Long
Short-Term Memory (BiLSTM) |
Combined CNN-BiLSTM model for
short-text sentiment analysis. Benefits from feature extraction capabilities
of CNN and short-term bidirectional text dependency learning capabilities of
BiLSTM. |
|
Performance
Comparison of Machine Learning Approaches (Poornima
and Priya, 2020) |
Performance
Comparison of Machine Learning Approaches |
SVM,
Multinomial Naïve Bayes, Logistic Regression |
Performance
comparison of SVM, Multinomial Naïve Bayes, and Logistic Regression for
sentiment analysis. Logistic Regression achieves high accuracy with a bigram
model. |
|
RNN for
Sentiment Analysis (Goel and Batra, 2020) |
RNN for Sentiment Analysis |
RNN, Machine learning methods |
Comparison of RNN and machine
learning methods for sentiment analysis. The superior performance of the RNN
model. |
Methods and Techniques
As was stated in the introduction, Sentiment Analysis has been done in a
variety of ways. These methods were divided into three groups: hybrid, machine
learning, and lexicon-based. After reading about multiple studies, only one
used a hybrid strategy; the others used lexicon- and machine-learning
techniques. In almost every study, machine learning methods are employed. The
most popular machine learning algorithm was the SVM. In addition, K-NN,
decision trees, and Naive Bayes classifiers were applied. Deep learning
approaches are very common nowdays as they’re very helpful in extracting
features. There are several methods and techniques used in sentiment analysis,
each with its own set of strengths and limitations. Some of the most used
methods and techniques are:
1. Machine learning: To predict the sentiment of new
text, machine learning techniques are employed to train models on massive
datasets of labeled text. Three
popular methods, Naive Bayes, SVM, and Random
Forests, are used for Sentiment Analysis. A large Volume of labeled data is
required for training these methods, which is a computationally costly process,
but these methods show the potential to be more
accurate than rule-based methods (Poornima and Priya, 2020; Bhatt and Swarndeep, 2020; Yaakub, Latiffi, and Zaabar, 2019; Wongkar and Angdresey, 2016; Berawi, 2020).
2. Complex data can be extracted using different CNNs
and RNNs, and they can be used to classify text at a more granular level, such
as identifying specific emotions or opinions. However, they require Vast
amounts of labeled data and can be computationally expensive (Tan, Lee, and Lim, 2023; Yue and Li, 2020)
3. Lexicon-based methods can be useful in cases where training data is
limited or when domain-specific knowledge is required. However, they can be
limited by the fact that they do not take context into account (Appiahene et al., 2022;
Hilario et al., 2021; Drus and Khalid, 2019)
Overall, the choice of method or technique for sentiment analysis will
depend on the specific application, available data, and resources. It is
important to carefully evaluate the strengths and limitations of each approach
before deciding which one to use.
5. Dataset Domains
Datasets are a crucial component of
sentiment analysis as they provide the labeled data necessary to train and
evaluate sentiment analysis models. It was discovered after analyzing the
articles that researchers employed many datasets in different fields.
E-commerce applications, movie reviews, tweets, books, items, political
battles, and online discussion forums like Quora are some of these domains. The
summaries of research papers give insights into the various dataset domains
utilized in sentiment analysis investigations. These domains cover a wide range
of themes, languages, and sources, demonstrating sentiment analysis's
adaptability and application across several areas.
The performance and contextual relevance
of sentiment analysis algorithms are substantially impacted by the selection of
datasets from domains. Sentiment analysis algorithms can comprehend sentiment
expressions, linguistic variants, and contextual nuances better by using
domain-specific datasets, which eventually produce more accurate and insightful
sentiment analysis results.
Challenges
Despite substantial advancements in
recent years, sentiment analysis still confronts several difficulties. The
following are some of the current difficulties in sentiment analysis:
1. Sentiment analysis models frequently have
trouble interpreting the nuances of language used in context, such as sarcasm,
irony, or metaphorical terms. These subtleties have a substantial impact on the
sentiment expressed in a text, and it is still difficult for sentiment analysis
to capture them correctly.
2. Multilingual sentiment analysis: Due to the
variations in language structures, sentiment expressions, and cultural cues
between various languages, sentiment analysis in multilingual contexts is
difficult. It is still difficult to create sentiment analysis algorithms that
are reliable and accurate and can handle different languages.
3. Domain-specific sentiment analysis: Sentiment
expressions and language usage might change dramatically across different
domains, such as product reviews, social media, or healthcare. Hence, sentiment
analysis models trained on broad datasets may not perform well in domain-specific
situations.
4. Managing noisy and unstructured data:
User-generated content, such as posts on social media, reviews on websites, and
other user-generated content, frequently contains noise, such as typos,
grammatical errors, and colloquial language. Given the lack of linguistic
standardization and consistency, sentiment analysis models must be strong
enough to manage such noisy and unstructured data.
5. Lack of labeled data: To be trained, sentiment
analysis models normally need a lot of labeled data. The acquisition of labeled
data, however, can be costly, time-consuming, and resource-intensive. The
absence of labeled data, particularly for certain topics or languages,
continues to be a barrier to the creation of precise and reliable sentiment
analysis models.
8. Future Directions
To develop the field of
sentiment analysis, various new paths are now being investigated. Several
potential paths for sentiment analysis in the future include:
1.
Context-aware sentiment analysis: Creating models
for sentiment analysis that can more accurately comprehend and take into
account a text's contextual information, such as the words around them, the
sentence structure, and the discourse context, in order to accurately capture
the nuanced sentiment expressions.
2.
Multimodal sentiment analysis: Extending sentiment
analysis beyond text and including other modalities, such as audio, visual, and
physiological inputs, in order to capture emotions and sentiments communicated
through many channels, including facial expressions, tone of voice, and
physiological reactions.
3.
Deep learning approaches: Exploring more advanced
deep learning techniques, such as transformer-based models, graph neural
networks, and reinforcement learning, to improve the accuracy and performance
of sentiment analysis models, particularly in capturing long-range dependencies
and understanding complex relationships among words and phrases.
4. Real-time sentiment analysis: Developing sentiment analysis models that can process and analyze sentiment in real time, allowing for real-time monitoring of social media, customer feedback, or other streams of data for timely decision-making and response.
Discussion
Most studies on sentiment
analysis (SA) were tailored to address industry-specific challenges because
SA-ready datasets available were often domain-specific, such as movie and
tweets datasets. Additionally, because most of the content published on various
platforms is written in various styles, creating a corpus takes time and needs
specially designed pre-processing tools for SA. Approaches based on machine
learning and deep learning were frequently employed to categorize text as good,
negative, or neutral. SVM and KNN supervise machine learning algorithms to yield
the highest accuracy. The major use of CNN and RNN is feature extraction from
text. When classifying large amounts of text data, it has been found that most
predictions based on sentiment analysis typically use Naive Bayes (NB) and
Neural Network (NN) algorithms.
Most of the research in sentiment analysis has focused on machine
learning and deep learning techniques to extract features from text and mine
messages for insightful information. Sentiment analysis is an effective tool
for analyzing user-generated information on social media websites, movie review
websites, and e-commerce websites and for helping people make better decisions.
However, properly detecting underlying sentiment is difficult due to the
intricacies of human language, cultural and contextual impacts, data sparsity
and noise, and a lack of universal sentiment markers. This survey's goal was to
examine historical patterns in sentiment analysis to assist researchers in
addressing issues and potential solutions. This is significant since various
fields rely heavily on sentiment analysis for decision-making. The main
resources, difficulties, and strategies for sentiment analysis that have been
created for data mining jobs were discussed in this review. Additionally, the
selection of available datasets is limited to a few industries, including
hotels, book reviews, social media comments, tweets, and online retail product
reviews. Furthermore, no multi-domain gold-standard dataset is currently
accessible. Such a dataset could be created in future research. Sentiment
Analysis can be used to profit from more studies and datasets.
We would like to thank the members of the Sunway
University Research Project (GRTIN-IGS-DCIS[S]-01-2022) for their contribution
and collaboration.
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