A beginners guide to natural language sentiment analysis

If so, these two groups behave fundamentally differently from one another and thus represent two distinct types of investors. Additionally, the results show that cryptocurrency enthusiasts began to tweet relatively more often after the cryptocurrency crash, suggesting that multiple behavioral changes occurred as a consequence of the crash. This provides further evidence that cryptocurrency enthusiasts and traditional investors are fundamentally different groups, with distinct responses to similar stimuli. Evidentiary, a classification of the specific textual content of tweets in each group, reveals evidence of herding behavior among cryptocurrency enthusiasts but not among traditional investors.

Unique concepts in each abstract are extracted using Meta Map and their pair-wise co-occurrence are determined. Then the information is used to construct a network graph of concept co-occurrence that is further analyzed to identify content for the new conceptual model. Medication adherence is the most studied drug therapy problem and co-occurred with concepts related to patient-centered interventions targeting self-management. The framework requires additional refinement and evaluation to determine its relevance and applicability across a broad audience including underserved settings.

• Sentiment analysis is great for quickly analyzing user’s opinion on products and services, and keeping track of changes in opinion over time.
• Since simple tokens may not represent the actual meaning of the text, it is advisable to use phrases such as “North Africa” as a single word instead of ‘North’ and ‘Africa’ separate words.
• Over the years, in subjective detection, the features extraction progression from curating features by hand to automated features learning.
• The latest versions of Driverless AI implement a key feature called BYOR[1], which stands for Bring Your Own Recipes, and was introduced with Driverless AI (1.7.0).
• In fact, it’s important to shuffle the list to avoid accidentally grouping similarly classified reviews in the first quarter of the list.

Using different libraries, developers can execute machine learning algorithms to analyze large amounts of text. The first step was to curate a list of Twitter users for the potential treatment and control groups. This approach was chosen over other sample selection methods (e.g., the seed-based method proposed by Yang et al. (2015)) because it allows for a straightforward classification of users. First, when the data for the study were collected, the Twitter API was freely accessible to researchers. Second, Twitter users tend to post frequently, with short yet expressive posts, which is an ideal combination for this study. Third, a body of literature exists on extracting a representative sample of users from Twitter for a given research purpose (Vicente 2023; Mislove et al. 2011).

Title:A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models

But with time the technology matures – especially the AI component –the computer will get better at “understanding” the query and start to deliver answers rather than search results. Initially, the data chatbot will probably ask the question ‘how have revenues changed over the last three-quarters? But once it learns the semantic relations and inferences of the question, it will be able to automatically perform the filtering and formulation necessary to provide an intelligible answer, rather than simply showing you data. The Robot uses AI techniques to automatically analyze documents and other types of data in any business system which is subject to GDPR rules.

Given the gradually increasing role of cryptocurrencies in traditional portfolios, a failure to regulate the cryptocurrency market could lead to spillovers to other markets and negatively impact all investors. Despite the fact that many cryptocurrencies (e.g., Bitcoin) have a history of bubbles (Chaim and Laurini 2019), many cryptocurrency enthusiasts routinely invest excessively in them. This seemingly irrational behavior can lead to people tying a large proportion of their financial well-being to cryptocurrency. Several companies in BI spaces are trying to get with the trend and trying hard to ensure that data becomes more friendly and easily accessible. But still there is a long way for this.BI will also make it easier to access as GUI is not needed.

As a human, you can read the first sentence and determine the person is offering a positive opinion about Air New Zealand. The second sentence is offering a negative opinion, and the last is also a negative opinion, although it’s a little harder to parse. Learn about the importance of mitigating bias in sentiment analysis and see how AI is being trained to be more neutral, unbiased and unwavering.

Stages in Natural Language Processing:

In this paper, we first distinguish four phases by discussing different levels of NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP. BERT predicts 1043 correctly identified mixed feelings comments in sentiment analysis and 2534 correctly identified positive comments in offensive language identification.

From the figure, it is observed that training accuracy increases and loss decreases. So, the model performs well for offensive language identification compared to other pre-trained models. The datasets using in this research work available from24 but restrictions apply to the availability of these data and so not publicly available. Data are however available from the authors upon reasonable request and with permission of24.

Emotion detection investigates and identifies the types of emotion from speech, facial expressions, gestures, and text. Sharma (2016) [124] analyzed the conversations in Hinglish means mix of English and Hindi languages and identified the usage patterns of PoS. Their work was based on identification of language and POS tagging of mixed script. They tried to detect emotions in mixed script by relating machine learning and human knowledge. They have categorized sentences into 6 groups based on emotions and used TLBO technique to help the users in prioritizing their messages based on the emotions attached with the message.

Introduction to Sentiment Analysis Covering Basics, Tools, Evaluation Metrics, Challenges, and Applications

In early 1980s computational grammar theory became a very active area of research linked with logics for meaning and knowledge’s ability to deal with the user’s beliefs and intentions and with functions like emphasis and themes. Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc.

In work of Clark et al. (2018) used Twitter tweets concerning patient’s experiences as an add-on to analyze public health. Over a year, they generated roughly five million breast cancer-related tweets using Twitter’s Streaming API. After pre-processing, the tweets were classified with a standard LR classifier and a CNN model. Positive treatment experiences, rallying support, and expanding public awareness were all linked. In conclusion, applying sentiment analysis to analyze patient-generated data on social media can help determine patients’ needs and views.

In the work of McDuff et al. (2014) have illustrated how webcams may be used to collect a large number of emotional reactions, including sentiment. While this degrades the audiovisual capture quality, it achieves a scale that is not conceivable in the laboratory. Additionally, there is the issue of labeling confidential laboratory data, which prohibits those permitted to examine the data from performing the time-consuming operation of labeling. As a result, they are restricted in terms of the amount of data they can collect in the laboratory and our ability to label huge volumes of data. There are several methods for assessing feelings, but word embedding algorithms such as word2vec and GloVe turn words into meaningful vectors.

Natural Language Processing (NLP) and Deep Learning are two rapidly growing fields that have gained immense popularity in recent years. NLP is a branch of artificial intelligence (AI) that deals with the interaction between computers and human languages, while deep learning is a subset of machine learning that uses neural networks to process complex data. Together, they have revolutionized the way machines understand and analyze human sentiment analysis natural language processing language. The first objective gives insights of the various important terminologies of NLP and NLG, and can be useful for the readers interested to start their early career in NLP and work relevant to its applications. The second objective of this paper focuses on the history, applications, and recent developments in the field of NLP. The third objective is to discuss datasets, approaches and evaluation metrics used in NLP.

It’s task was to implement a robust and multilingual system able to analyze/comprehend medical sentences, and to preserve a knowledge of free text into a language independent knowledge representation [107, 108]. The goal of NLP is to accommodate one or more specialties of an algorithm or system. The metric of NLP assess on an algorithmic system allows for the integration of language understanding and language generation. Rospocher et al. [112] purposed a novel modular system for cross-lingual event extraction for English, Dutch, and Italian Texts by using different pipelines for different languages.

As in the previous subsection, these results confirm and build on the literature that links investor sentiment and market conditions. Cryptocurrency enthusiasts are prone to express themselves in sadder and more negative ways, with less trust, joy, anger, disgust, fear, and surprise than traditional investors. This suggests that a certain type of person (i.e., a certain set of personality traits) self-selects into a herding-type cryptocurrency group. The DID estimators estimated in this study are best interpreted as the magnitude of the differential response to the cryptocurrency crash between cryptocurrency enthusiasts and traditional investors.

Data can be collected from various sources like Twitter, news articles, blogs, etc. Sentence level sentiment analysis can be done on these texts, after which the overall polarity of texts will be decided of news of a particular company. In work of Xing et al. (2018) used to determine whether the trend will be rising or decreasing. Positive news tended to lead to an upward trend, whereas negative news tended to lead to a downward trend. Bitcoin and other digital cryptocurrencies relate to a novel technology known as Blockchain. Participants inside the blockchain network verify the digital transactions using peer to peer consensus methods.

This gives us a little insight into, how the data looks after being processed through all the steps until now. But, for the sake of simplicity, we will merge these labels into two classes, i.e. Now you’ve reached over 73 percent accuracy before even adding a second feature! While this doesn’t mean that the MLPClassifier will continue to be the best one as you engineer new features, having additional classification algorithms at your disposal is clearly advantageous.

Magnifying this concern, Vidal-Tomás et al. (2019) showed that herding behavior among cryptocurrency investors is particularly strong in down markets. Cryptocurrencies have grown rapidly in popularity, especially among non-traditional investors (Mattke et al. 2021). Consequently, the motivations underlying the decisions of many cryptocurrency investors are not always purely financial, with investors exhibiting substantial levels of herding behavior with respect to cryptocurrencies (Ooi et al. 2021). In fact, the culture developing around cryptocurrency enthusiasts engaging in herding behavior is rich and complex (Dodd 2018).

Ahonen et al. (1998) [1] suggested a mainstream framework for text mining that uses pragmatic and discourse level analyses of text. We first give insights on some of the mentioned tools and relevant work done before moving to the broad applications of NLP. In the total amount of predictions, the proportion of accurate predictions is called accuracy and is derived in the Eq. The proportion of positive cases that were accurately predicted is known as precision and is derived in the Eq. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.

In the next section, you’ll build a custom classifier that allows you to use additional features for classification and eventually increase its accuracy to an acceptable level. Different corpora have different features, so you may need to use Python’s help(), as in help(nltk.corpus.tweet_samples), or consult NLTK’s documentation to learn how to use a given corpus. These methods allow you to quickly determine frequently used words in a sample.

Part of Speech tagging is the process of identifying the structural elements of a text document, such as verbs, nouns, adjectives, and adverbs. Book a demo with us to learn more about how we tailor our services to your needs and help you take advantage of all these tips & tricks. For a more in-depth description of this approach, I recommend the interesting and useful paper Deep Learning for Aspect-based Sentiment Analysis by Bo Wanf and Min Liu from Stanford University. We’ll go through each topic and try to understand how the described problems affect sentiment classifier quality and which technologies can be used to solve them.

For instructions on installing the gcloud CLI,

setting up a project with a service account

see the Quickstart. In a nutshell, if the sequence is long, then RNN finds it difficult to carry information from a particular time instance to an earlier one because of the vanishing gradient problem. Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task. We will also remove the code that was commented out by following the tutorial, along with the lemmatize_sentence function, as the lemmatization is completed by the new remove_noise function. In addition to this, you will also remove stop words using a built-in set of stop words in NLTK, which needs to be downloaded separately.

Set up Twitter API credentials

BERT is an extension of the Transformers model proposed (Vaswani et al. 2017) in the “Attention is all you need” paper. BERT uses transformers, an attention mechanism that learns contextual relationships between words or sub-words in a given text. The input in this model contains the word embeddings and position embeddings, unlike transformers, but also has an extra vector representing the sentence it belongs to handle two or more sentences at a time. BERT consists of encoders based transformers; the encoder part is similar to the transformer encoder. BERT has two models BERT base with 12 encoders stacked with 110 million parameters and BERT large model with 24 encoders stacked with 330 million parameters.

VADER is particularly effective for analyzing sentiment in social media text due to its ability to handle complex language such as sarcasm, irony, and slang. It also provides a sentiment intensity score, which indicates the strength of the sentiment expressed in the text. Python is a popular programming language for natural language processing (NLP) tasks, including sentiment analysis. Sentiment analysis is the process of determining the emotional tone behind a text. There are considerable Python libraries available for sentiment analysis, but in this article, we will discuss the top Python sentiment analysis libraries. These relevant aspects of tweets are referred to as affective states in the sentiment analysis literature (Xie et al. 2021) as a “positive,” “negative,” “neutral,” and an aggregate or “compound” score.

Unlike traditional machine learning techniques that require handcrafted features, deep learning models can learn feature representations directly from raw text data. This allows them to capture complex patterns and relationships between words and phrases, making them ideal for sentiment analysis tasks. Deep learning techniques have further enhanced NLP by allowing machines to learn from vast amounts of data without being explicitly programmed for each task. This makes them suitable for handling natural language tasks that involve large datasets and complex patterns. By using multiple layers of artificial neural networks, deep learning models can perform tasks like language translation, summarization, question answering systems, sentiment analysis, chatbots,and more with remarkable accuracy.

The pretrained models like Logistic regression, CNN, BERT, RoBERTa, Bi-LSTM and Adapter-Bert are used text classification. The classification of sentiment analysis includes several states like positive, negative, Mixed Feelings and unknown state. Similarly for offensive language identification the states include not-offensive, offensive untargeted, offensive targeted insult group, offensive targeted insult individual and offensive targeted insult other. Finally, the results are classified into respective states and the models are evaluated using performance metrics like precision, recall, accuracy and f1 score.

Hybrid techniques typically achieve excellent performance and accuracy through the use of many approaches. Numerous hybrid feature selection algorithms for sentiment analysis have been developed (Chiew et al. 2019). Wrapper approach This approach is based on machine learning algorithms since it relies on the output of the machine learning algorithm. Approaches are often iterative and computationally demanding due to this dependency, but they can determine the optimal feature set for that particular modeling algorithm.

In this step you removed noise from the data to make the analysis more effective. In the next step you will analyze the data to find the most common words in your sample dataset. Noise is specific to each project, so what constitutes noise in one project may not be in a different project. They are generally irrelevant when processing language, unless a specific use case warrants their inclusion.

Scikit-learn has a simple interface for sentiment analysis, making it a good choice for beginners. Scikit-learn also includes many other machine learning tools for machine learning tasks like classification, regression, clustering, and dimensionality reduction. Turning to the effects of investor sentiment on cryptocurrencies, the literature remains plentiful. Cryptocurrencies do not always respond to new information in the same manner as traditional investments https://chat.openai.com/ Rognone et al. (2020). This is particularly important because the sentiment analysis of both news (Lamon et al. 2017) and social media (Philippas et al. 2019) has been linked to changes in cryptocurrency prices. Mai et al. (2018) built on these results by showing that not only did social media sentiment affect cryptocurrency markets but also that such effects were driven by the sentiment of low-frequency posters, not high-frequency posters.

Subjectivity tagged with the knowledge relating to both identity and orientation of attitude holder. In work of Bordes et al. (2014), Bhaskar et al. (2015), Rao and Ravichandran (2009) worked on the WordNet dataset in their work. They determined that the viewer’s subjectivity and the actor’s subjectivity might be distinguished in some instances (Hershcovich and Donatelli 2021). By using sentiment analysis to conduct social media monitoring brands can better understand what is being said about them online and why.

Though NLP tasks are obviously very closely interwoven but they are used frequently, for convenience. Some of the tasks such as automatic summarization, co-reference analysis etc. act as subtasks that are used in solving larger tasks. Nowadays NLP is in the talks because of various applications and recent developments although in the late 1940s the term wasn’t even in existence. So, it will be interesting to know about the history of NLP, the progress so far has been made and some of the ongoing projects by making use of NLP.

For instance, the line “This movie is good.” is a positive sentence, but “The movie is not good.” is a negative sentence. Regrettably, some systems eliminate negation words because they are included in stop word lists or are implicitly omitted since they have a neutral sentiment value in a lexicon and do not affect the absolute polarity. However, reversing the polarity is not straight forward because negation words might occur in a sentence without affecting the text’s emotion. “Deep learning uses many-layered neural networks that are inspired by how the human brain works,” says IDC’s Sutherland. This more sophisticated level of sentiment analysis can look at entire sentences, even full conversations, to determine emotion, and can also be used to analyze voice and video. Companies use sentiment analysis to evaluate customer messages, call center interactions, online reviews, social media posts, and other content.

In fact, MT/NLP research almost died in 1966 according to the ALPAC report, which concluded that MT is going nowhere. But later, some MT production systems were providing output to their customers (Hutchins, 1986) [60]. By this time, work on the use of computers for literary and linguistic studies had also started. As early as 1960, signature work influenced by AI began, with the BASEBALL Q-A systems (Green et al., 1961) [51].

For instance, consider the word “thong” which means flip-flops or slippers in Australia but means undergarments in the UK. Similarly, different spellings for the same word, such as “color” and “colour,” mean the same but are spelled differently in different regions. This will create duplicates and may affect the accuracy and computational cost of the model. There are thousands of languages spoken worldwide, although NLP techniques are hardly available to 5-10 languages, and resources are widely available for English. Models like SVM, NB are not computationally costly, but neural networks and attention models have shown that they are computationally costly.

The classification report shows that our model has an 84% accuracy rate and performs equally well on both positive and negative sentiments. A large amount of data that is generated today is unstructured, which requires processing to generate insights. Some examples of unstructured data are news articles, posts on social media, and search history. The process of analyzing natural language and making sense out of it falls under the field of Natural Language Processing (NLP). Sentiment analysis is a common NLP task, which involves classifying texts or parts of texts into a pre-defined sentiment.

The first objective of this paper is to give insights of the various important terminologies of NLP and NLG. Some authors recently explored with code-mixed language to identify sentiments and offensive contents in the text. You can foun additiona information about ai customer service and artificial intelligence and NLP. Similar results were obtained using ULMFiT trained on all four datasets, with TRAI scoring the highest at 70%. For the identical assignment, BERT trained on TRAI received a competitive score of 69%.

BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model for natural language processing developed by Google. Sentiment analysis is a process in Natural Language Processing that involves detecting and classifying emotions in texts. The emotion is focused on a specific thing, an object, an incident, or an individual. Although some tasks are concerned with detecting the existence of emotion in text, others are concerned with finding the polarities of the text, which is classified as positive, negative, or neutral. The task of determining whether a comment contains inappropriate text that affects either individual or group is called offensive language identification. The existing research has concentrated more on sentiment analysis and offensive language identification in a monolingual data set than code-mixed data.

The cross-language analysis is done similarly by training the model on a dataset from a source language and then evaluating it on a dataset from a different language with limited data. The ambiguity of word polarity is one of the obstacles that sentiment analysis must overcome. In the work of Vechtomova (2017) and Singh et al. (2021b) demonstrated that retrieval-based models provide an alternative to Machine Learning based strategies for word polarity detection.

The objective of this section is to present the various datasets used in NLP and some state-of-the-art models in NLP. CapitalOne claims that Eno is First natural language SMS chatbot from a U.S. bank that allows customers to ask questions using natural language. Customers can interact with Eno asking questions about their savings and others using a text interface. Chat GPT This provides a different platform than other brands that launch chatbots like Facebook Messenger and Skype. They believed that Facebook has too much access to private information of a person, which could get them into trouble with privacy laws U.S. financial institutions work under. Like Facebook Page admin can access full transcripts of the bot’s conversations.

While this difference may seem small, it helps businesses a lot to judge and preserve the amount of resources required for improvement. While this consequence is incredibly important, there is another potential consequence of these results. This is particularly significant as the deliberate, collectivist approach to publicly displaying positivity and holding Bitcoin (“wagmi”) could have mitigated the magnitude of the crash to a small extent. These findings are also important as they provide further support that cryptocurrency enthusiasts will hold on to a cryptocurrency even when they could earn better returns by investing elsewhere.

In future, to increase system performance multitask learning can be used to identify sentiment analysis and offensive language identification. The majority of sentiment analysis in the modern day is data-driven machine learning models adapting a sentiment analysis algorithm developed for product evaluations to evaluate microblog postings is an unanswered question. Additionally, how to deal with ambiguous situations and irony are key difficulties in sentiment analysis. For instance, a sarcastic remark about an object is intended to communicate a negative sentiment; yet, conventional sentiment analysis algorithms frequently miss this meaning. Numerous methods have been proposed (Castro et al. 2019; Medhat et al. 2014) for detecting sarcasm in language.

In the work of Rognone et al. (2020) investigated the influence of news sentiment on cryptocurrencies like bitcoin and other standard currencies volatility, volume, and returns. In the work of Park and Kim (2016) used a corpus based method for sentiment analysis. They used linguistic constraints and connectives to find the sentiment of a new token. For instance, tokens on either side of correlative conjunctions like “AND” tend to have the same orientation while words like “OR”, but point out opinion change or the tokens on opposite orientations. Although this idea is popularly known as Sentiment Consistency, in practice, this is not that consistent.

Sentiment analysis is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People’s opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity.

Terms frequency It is one of the simplest ways to express features that are more frequently used in various NLP applications, including Sentiment Analysis, for information retrieval. It considers a single word, i.e., uni-gram or group of two-three words, which can be in bi-gram and tri-gram, with their terms count representing features (Sharma et al. 2013). Term frequency is the integer value, which is its count in the given document. TF-IDF can be used as a weighted scheme for better results that will measure the importance of any token in the given document.

This section analyses the performance of proposed models in both sentiment analysis and offensive language identification system by examining actual class labels with predicted one. The first sentence is an example of a Positive class label in which the model gets predicted correctly. The same is followed for all the classes such as positive, negative, mixed feelings and unknown state. In recent years, classification of sentiment analysis in text is proposed by many researchers using different models, such as identifying sentiments in code-mixed data9 using an auto-regressive XLNet model.

Pragmatic features are those that emphasize the application of words rather than a methodological foundation. Pragmatics is the study of how context relates to perception in linguistics and related sciences. Pragmatics is the study of phenomena such as implicature, speech acts, relevance, and conversations.

From the figure it is observed that training accuracy increases and loss decreases. So, the model performs well for sentiment analysis when compared to other pre-trained models. Precision, Recall, Accuracy and F1-score are the metrics considered for evaluating different deep learning techniques used in this work. Offensive language is any text that contains specific types of improper language, such as insults, threats, or foul phrases. This problem has prompted various researchers to work on spotting inappropriate communication on social media sites in order to filter data and encourage positivism.

Datasets used in NLP and various approaches are presented in Section 4, and Section 5 is written on evaluation metrics and challenges involved in NLP. In 2018, Google AI Language Researchers open-sourced a new model for NLP called BERT. It has a breakthrough and has taken the industry of deep learning by storm due to its performance. In the work of Han et al. (2021) Transformer network revolutionized the area of NLP and replaced the usage of LSTM and Bi-LSTM. The main advantage is that Transformers do not suffer from vanishing or exploding gradient problems as they do not use recurrence at all, and also, they are faster and less expensive to train.

Gaining a proper understanding of what clients and consumers have to say about your product or service or, more importantly, how they feel about your brand, is a universal struggle for businesses everywhere. Social media listening with sentiment analysis allows businesses and organizations to monitor and react to emerging negative sentiments before they cause reputational damage. This helps businesses and other organizations understand opinions and sentiments toward specific topics, events, brands, individuals, or other entities. Similarly, in customer service, opinion mining is used to analyze customer feedback and complaints, identify the root causes of issues, and improve customer satisfaction. Natural language processing (NLP) is one of the cornerstones of artificial intelligence (AI) and machine learning (ML).

Global Vectors (GloVe) Global Vectors for word representation have developed (Pennington et al. 2014) by an unsupervised learning approach to generate word embeddings from a corpus word-to-word co-occurrence matrix. GloVe is a popularly used method as it is straightforward and quick to train GloVe model because of its parallel implementation capacity (Al Amrani et al. 2018). The growth of social network sites has generated a slew of fields devoted to analyzing these networks and their contents in order to extract necessary information.

Sentiment analysis is concerned with deriving the sentiments communicated by a piece of text from its content. Sentiment analysis is a subfield of NLP and that, given long and illustrious public opinion for decision making, there must be multiple early works addressing it. However, it still works going on sentiment analysis develop till the new millennium. Watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently.

The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors. In particular, cryptocurrency enthusiasts’ tweets became more neutral and, surprisingly, less negative. This result appears to be primarily driven by a deliberate, collectivist effort to promote positivity within the cryptocurrency community (“wagmi”).

Also the topic of detecting opinion spam and fraudulent reviews was investigated. Additionally, In the work of Yue et al. (2019) and Liu et al. (2012) conducted research on the effectiveness of internet reviews. The Authors (Jain et al. 2021b) discuss machine learning applications that incorporate online reviews in sentiment categorization, predictive decision-making, and the detection of false reviews.