Sentiment Analysis Tools Research Articles

Fuzzy ensemble of fined tuned BERT models for domain-specific sentiment analysis of software engineering dataset.

Software engineers post their opinions about various topics on social media that can be collectively mined using Sentiment Analysis. Analyzing this opinion is useful because it can provide insight into developers' feedback about various tools and topics. General-purpose sentiment analysis tools do not work well in the software domain because most of these tools are trained on movies and review datasets. Therefore, efforts are underway to develop domain-specific sentiment analysis tools for the Software Engineering (SE) domain. However, existing domain-specific tools for SE struggle to compute negative and neutral sentiments and can not be used on all SE datasets. This work uses a hybrid technique based on deep learning and a fine-tuned BERT model, i.e., Bert-Base, Bert-Large, Bert-LSTM, Bert-GRU, and Bert-CNN presented that is adapted as a domain-specific sentiment analysis tool for Community Question Answering datasets (named as Fuzzy Ensemble). Five different variants of fine-tuned BERT on the SE dataset are developed, and an ensemble of these fine-tuned models is taken using fuzzy logic. The trained model is evaluated on four publicly available benchmark datasets, i.e., Stack Overflow, JavaLib, Jira, and Code Review, using various evaluation metrics. The fuzzy Ensemble model is also compared with the state-of-the-art sentiment analysis tools for the software engineering domain, i.e., SentiStrength-SE, Senti4SD, SentiCR, and Generative Pre-Training Transformer (GPT). GPT mode is fine-tuned by the authors for domain-specific sentiment analysis. The Fuzzy Ensemble model covers the limitation of existing tools and improve accuracy to predict neutral sentiments even on diverse dataset. The fuzzy Ensemble model performs superior to state-of-the-art tools by achieving a maximum F1-score of 0.883.

Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework

In the era of social media, the use of emojis and code-mixed language has become essential in online communication. However, selecting the appropriate emoji that matches a particular sentiment or emotion in the code-mixed text can be difficult. This paper presents a novel task of predicting multiple emojis in English-Hindi code-mixed sentences and proposes a new dataset called SENTIMOJI, which extends the SemEval 2020 Task 9 SentiMix dataset. Our approach is based on exploiting the relationship between emotion, sentiment, and emojis to build an end-to-end framework. We replace the self-attention sublayers in the transformer encoder with simple linear transformations and use the RMS-layer norm instead of the normal layer norm. Moreover, we employ Gated Linear Unit and Fully Connected layers to predict emojis and identify the emotion and sentiment of a tweet. Our experimental results on the SENTIMOJI dataset demonstrate that the proposed multi-task framework outperforms the single-task framework. We also show that emojis are strongly linked to sentiment and emotion and that identifying sentiment and emotion can aid in accurately predicting the most suitable emoji. Our work contributes to the field of natural language processing and can help in the development of more effective tools for sentiment analysis and emotion recognition in code-mixed languages. The codes and data will be available at https://www.iitp.ac.in/~ai-nlp-ml/resources.html#SENTIMOJI to facilitate research.

COMPARATIVE EFFECTIVENESS OF RULE-BASED AND MACHINE LEARNING METHODS IN SENTIMENT ANALYSIS OF KAZAKH LANGUAGE TEXTS

Sentiment analysis is increasingly pivotal in natural language processing (NLP), crucial for deciphering public opinions across diverse sectors. This research conducts a comparative examination of rule-based and machine learning (ML) methods in sentiment analysis, specifically targeting the Kazakh language. Given the Kazakh language's limited exposure in computational linguistics, the study meticulously evaluates datasets from news articles, literature, and Amazon product reviews, aiming to compare the efficiency, adaptability, and overall performance of these distinct approaches. Employing a detailed set of evaluation metrics such as accuracy, precision, recall, and computational efficiency, the study provides a comprehensive analysis of the strengths and limitations of rule-based techniques versus ML models like Logistic Regression, Multinomial Naive Bayes, Decision Trees, Random Forest, and XGBoost. The findings suggest rule-based methods excel in identifying nuanced emotional expressions within literary texts, while ML models demonstrate superior adaptability and robustness, particularly effective in handling the linguistic variations found in news and reviews. Despite the strengths identified, the study also reveals significant limitations of the rule-based approach, especially in broader contexts beyond literary analysis. This highlights an imperative for future research to integrate sentiment dictionaries or domain-specific lexicons that cater to a wider array of linguistic styles, potentially enhancing sentiment analysis tools' applicability in Kazakh and similar less-studied languages. This investigation contributes significantly to the sentiment analysis discourse, offering invaluable insights for both researchers and practitioners by elucidating the complexities of applying NLP technologies across diverse linguistic landscapes, thus advancing the understanding and methodologies of sentiment analysis in the Kazakh language context.

Deciphering Public Sentiment on iPhone 14: A BERT-Based Analysis of Twitter Discourse

The study delves into understanding the sentiment of Twitter users towards the iPhone 14 by employing sentiment analysis models. Recognizing the limitations of traditional sentiment analysis tools, the research utilizes the BERT model, known for its bidirectional understanding of textual context. Initially, the study observed a prevailing negative sentiment using general models. However, after refining the approach using BERT, a more balanced representation of sentiments was observed. The BERT model's results highlighted both positive and negative reactions towards the iPhone 14 on Twitter, with distinct peaks in sentiment scores. The research underscores the importance of leveraging advanced models like BERT for tasks requiring nuanced understanding, providing stakeholders in the technology domain with comprehensive insights into public sentiment.

Appraisal or Critique? Analyzing Sentiments in Google Play Store Reviews with Python

In today's world of mobile applications, understanding how users feel about your app is key for developers and businesses looking to enhance their offerings. Our project takes a people-centric approach to analyzing sentiments expressed in Google Play Store reviews. We have crafted a comprehensive method using Python to delve into user feedback. We rely on the VADER sentiment analysis tool to gauge the emotional tone of reviews, sorting them into positive, negative, or neutral categories. Our code covers everything from gathering data to preprocessing, sentiment analysis, and visualization. This allows users to extract valuable insights from the feedback loop. By deciphering the sentiments within these reviews, our project becomes a potent resource for developers and businesses, empowering them to refine user experiences and make informed decisions. Our Python-based solution is not just powerful but also flexible and efficient, capable of handling large volumes of data. This approach contributes to more informed app development strategies, fostering better user satisfaction and business success.

The efficacy of digital platforms in the development and relevance of fashion business: Aftermath of COVID-19

The purpose of the study was to assess how well fashion companies were able to sustain themselves throughout the COVID-19 pandemic by leveraging internet platforms. In spite of the numerous disruptions the fashion industry has experienced as a result of the COVID-19 pandemic's effects on the workplace and the shift in consumer behaviour towards digital platforms, industry stakeholders think that digital platforms offer potential for the industry's revival because many fashion businesses were forced to close during the pandemic. The paper examines and summarises the extent to which digital platforms have been effective in fostering the long-term expansion of fashion companies and their influence on livelihoods in the COVID-19 pandemic, using a narrative literature approach. Based on productive interactions and transactions, digital platforms provide a 21st-century, cost-effective way for businesses to promote themselves. Prior to the pandemic, fashion companies were able to market their goods and services, track live events, keep an eye on trends and projections, and boost their online presence, all of which contributed to increased revenue. Notwithstanding the fact that they continue to face major obstacles that negatively impact the livelihoods of their employees, the study shows how digital platforms have been incredibly effective in helping fashion businesses, especially those in the United States, the United Kingdom, Europe, and Asia, to endure and flourish during COVID-19. Although African customers have become accustomed to online shopping and e-commerce, digital platforms have not been especially popular throughout the continent, as fashion designers and businesses have not properly utilised them. In order to promote fashion businesses and designers, governments and policymakers should support the development of digital infrastructure and digital literacy initiatives. Additionally, fashion brands and businesses in the supply chain should improve their knowledge and understanding of how consumers and potential clients communicate and transact in their online networks. Finally, fashion businesses and designers should invest in social media sentiment analysis tools to facilitate effective online visibility, seamless interactions, and transactions with consumers.

Prioritizing user requirements for digital products using explainable artificial intelligence: A data-driven analysis on video conferencing apps

The advent of Industry 5.0 has brought a wealth of digital information to mobile app stores. With the help of emerging technologies such as machine learning and explainable artificial intelligence (XAI), these large amounts of user-generated data can be efficiently captured and analyzed. In this study, we propose an app store analysis framework and demonstrate the utility of the framework by mining and prioritizing user requirements in three popular video conferencing apps. We used the Sentistrength sentiment analysis tool, structural topic modeling, the Gephi web analysis tool, machine learning, and XAI techniques to conduct an in-depth analysis of user requirements in Microsoft Teams, ZOOM Cloud Meetings, and Google Meet. The findings indicated that Steal data, Audio and video quality, Customer service, Hacker issues, Meeting and account passwords, Mute and unmute, Features, and Office platform were the web conferencing system's key areas for improvement. The study demonstrated the usability of app store analysis frameworks and the great potential of XAI to provide insights about requirements prioritization by interpreting machine learning models. Additionally, it offered valuable suggestions for app developers on using the massive data in app stores to improve their apps.

A two-actor model for understanding user engagement with content creators: Applying social capital theory

The emergence of video sharing platforms has given rise to the creation and consumption of tourism-related content. However, there is limited knowledge about the characteristics of content creators that enhance users' engagement with their content. The present study aims to fill this gap by examining creator characteristics and their impact on three tiers of user engagement. Tourism-related content, comprising 366 videos across six destinations, was extracted from YouTube using three social media analytic tools: VidIQ, TubeBuddy, and SocialBlade. The data were analyzed using PLS-SEM with SmartPLS 4.0. The findings reveal that channel subscribers positively influence user engagement at three levels – views, likes, and comments. However, a higher number of video uploads negatively impacts engagement. Furthermore, older videos tend to garner more views, but users' tendency to like the videos decreases over time. In addition, we extracted 23,993 comments and performed sentiment analysis on users’ comments using Python-based VADER social media sentiment analysis tool. The compound-based sentiment analysis reveals that 59.5 percent of users show positive sentiments toward tourism-related content on YouTube while only 9.3 comments were negative, and 31.2 percent of sentiments remain neutral. Temporal analysis shows the rising trend in qualitative user engagement from 2010 to 2023, highlighting a growing interest in consuming and interacting with tourism-related content. This study discusses its theoretical contributions and managerial implications for content creators, destination managers, and advertising agencies.

Artificial Intelligence-Powered Sentiment Analysis Tool to Assess the Popularity of Political Leaders in Informal Communities

This study investigates the utility of Artificial Intelligence sentiment analysis tools in assessing the popularity of political leaders within informal communities. In contemporary socio-political landscapes, understanding public sentiment towards political figures is paramount for gauging societal perceptions and predicting electoral outcomes. Leveraging sentiment analysis techniques, this research delves into the nuanced expressions and sentiments prevalent in informal online communities regarding political leaders. By analyzing textual data from social media platforms, forums, and other digital sources, the study aims to discern patterns and trends indicative of public opinion dynamics. Through natural language processing and machine learning algorithms, sentiment analysis tools enable the automated extraction of sentiments, emotions, and attitudes towards political leaders, providing valuable insights into their perceived popularity and public reception. The findings contribute to our understanding of the intersection between technology, politics, and public opinion, shedding light on the evolving landscape of political discourse in digital spaces. The study underscores the significance of sentiment analysis for political analysts, policymakers, and researchers in comprehending and navigating the complexities of contemporary political environments.

Agile fusion: developing 'Eat at Right Place' sentiment analysis tool

This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements.

Exploring Stress Detection in Tweets: A Comparative Business Analysis of Classification Algorithms

Abstract: In today's digitally-driven landscape, social media platforms such as Twitter, Facebook, and WordPress have emerged as critical conduits for public discourse, resulting in an overwhelming influx of unprocessed data, particularly on Twitter. Coping with the sheer volume of this data presents a significant challenge. To address this, sentiment analysis, a technique that categorizes sentiments into positive, negative, or neutral, offers a promising solution. This study delves into three main approaches for sentiment analysis: Machine learning-based methods, Sentiment lexicon-based approaches, and Hybrid methods. Its primary research objectives revolve around the identification of suitable algorithms and metrics for evaluating the performance of Machine Learning Classifiers. Additionally, the study aims to compare these metrics with respect to the dataset size, gauging their impact on the most appropriate sentiment analysis algorithm.The research methodology applied is experimental, entailing a rigorous assessment of algorithms using carefully selected metrics. The results of this investigation spotlight Naïve Bayes, Random Forest, XGBoost, and CNN-LSTM as the leading machine learning algorithms under consideration. These algorithms are assessed based on key performance metrics such as precision, accuracy, F1 score, and recall. Notably, the CNN-LSTM model emerges as the optimal choice for sentiment analysis of Twitter data within the specified dataset size, achieving a remarkable accuracy rate of 88%.In summary, this research successfully pinpoints the most suitable algorithm for sentiment analysis of Twitter data, especially in the context of dataset size. The CNN- LSTM model showcases its effectiveness, serving as a robust tool for sentiment analysis and delivering an impressive accuracy rate. This study significantly enhances our comprehension of public sentiment on Twitter, providing valuable insights into the ever-evolving realm of digital discourse and the analysis of vast unprocessed data

The evolution of integrated popular financial reporting: toward a digital-driven collaborative approach using sentiment analysis tool

PurposeThe research aims to provide a longitudinal case study to understand how digital transformation can be embedded in municipal reporting frameworks. The central role of such technology becomes increasingly evident as citizens demand greater transparency and engagement between them and governing institutions.Design/methodology/approachUtilising a longitudinal case study methodology, the research focusses on Turin’s Integrated Popular Financial Report (IPFR) as a lens through which to evaluate the broader implications of digital transformation on governmental transparency and operational efficiency.FindingsDigital tools, notably sentiment analysis, offer promising avenues for enhancing governmental efficacy and citizenry participation. However, persistent challenges highlight the inadequacy of traditional, inflexible reporting structures to cater to dynamic informational demands.Practical implicationsEmbracing digital tools is an imperative for contemporary public administrators, promoting streamlined communication and dismantling bureaucratic obstructions, all while catering to the evolving demands of an informed citizenry.Originality/valueDifferent from previous studies that primarily emphasised technology’s role within budgeting, this research uniquely positions itself by spotlighting the transformative implications of digital tools during the reporting phase. It champions the profound value of fostering bottom-up dialogues, heralding a paradigmatic shift towards co-creative public management dynamics.

"I’ve Got a Feeling": Performing Sentiment Analysis on Critical Moments in Beatles History

Our project involved the use of optical character recognition (OCR) and sentiment analysis tools to assess popular feelings regarding the Beatles and to determine how aggregated sentiment measurements changed over time in response to pivotal events during the height of their musical career. We used Tesseract to perform optical character recognition on historical newspaper documents sourced from the New York Times and smaller publications, leveraging advances in computer vision to circumvent the need for manual transcription. We employed state-of-the-art sentiment analysis models, including VADER, TextBlob, and SentiWordNet to obtain sentiment analysis scores for individual articles (Hutto and Gilbert 2014; TextBlob, n.d.; Baccianella, Esuli, and Sebastiani 2010). After selecting articles mentioning the group, we examined the changes in average sentiments displayed in articles corresponding to critical moments in the Beatles’ musical career to determine the impact of these events.

Public Perception of Online P2P Lending Applications

This study examines significant topics and customer sentiments conveyed in reviews of P2P lending applications (apps) in India by employing topic modeling and sentiment analysis. The apps considered are LenDenClub, Faircent, i2ifunding, India Money Mart, and Lendbox. Using Latent Dirichlet Allocation, we identified and labeled 11 topics: application, document, default, login, reject, service, CIBIL, OTP, returns, interface, and withdrawal. The sentiment analysis tool VADER revealed that most users have positive attitudes toward these apps. We also compared the five apps overall and on specific topics. Overall, LenDenClub had the highest proportion of positive reviews. We also compared the prediction abilities of six machine-learning models. Logistic Regression demonstrates high accuracy with all three feature extraction techniques: bag of words, term frequency-inverse document frequency, and hashing. The study assists borrowers and lenders in choosing the most appropriate application and supports P2P lending platforms in recognizing their strengths and weaknesses.

Negative affect variability differs between anxiety and depression on social media.

Negative affect variability is associated with increased symptoms of internalizing psychopathology (i.e., depression, anxiety). The Contrast Avoidance Model (CAM) suggests that individuals with anxiety avoid negative emotional shifts by maintaining pathological worry. Recent evidence also suggests that the CAM can be applied to major depression and social phobia, both characterized by negative affect changes. Here, we compare negative affect variability between individuals with a variety of anxiety and depression diagnoses by measuring the levels and degree of change in the sentiment of their online communications. Participants were 1,853 individuals on Twitter who reported that they had been clinically diagnosed with an anxiety disorder (A cohort, n = 896) or a depressive disorder (D cohort, n = 957). Mean negative affect (NA) and negative affect variability were calculated using the Valence Aware Dictionary for Sentiment Reasoning (VADER), an accurate sentiment analysis tool that scores text in terms of its negative affect content. Findings showed differences in negative affect variability between the D and A cohort, with higher levels of NA variability in the D cohort than the A cohort, U = 367210, p < .001, r = 0.14, d = 0.25. Furthermore, we found that A and D cohorts had different average NA, with the D cohort showing higher NA overall, U = 377368, p < .001, r = 0.12, d = 0.21. Our sample is limited to individuals who disclosed their diagnoses online, which may involve bias due to self-selection and stigma. Our sentiment analysis of online text may not completely capture all nuances of individual affect. Individuals with depression diagnoses showed a higher degree of negative affect variability compared to individuals with anxiety disorders. Our findings support the idea that negative affect variability can be measured using computational approaches on large-scale social media data and that social media data can be used to study naturally occurring mental health effects at scale.

Sentiment Analysis of Finnish Twitter Discussions on COVID-19 During the Pandemic

With the outbreak of the COVID-19 pandemic, researchers have studied how people reacted on social media during the pandemic. Sentiment analysis has been leveraged to gain insight. However, much of the research conducted on both sentiment analysis and social media analysis of COVID-19 often focuses on widespread languages, such as English and Chinese. This is partly due to the scarcity of resources for natural language processing and sentiment analysis for morphologically complex and less prevalent languages such as Finnish. This paper aims to analyze sentiments on Twitter in the Finnish language during the COVID-19 pandemic. We manually annotate with sentiments a random sample of 1943 tweets about COVID-19 in Finnish. We use it to build binomial and multinomial logistic regression models with Lasso penalty by exploiting ngrams and two existing sentiment lexicons. We also build two similar models using an existing (pre-COVID-19) Twitter dataset for comparison. The best-performing model for the Finnish language is then used to determine the trends of positive, negative, and neutral opinions on a collection of tweets in Finnish extracted between April 21 and June 18, 2020. The best sentiment polarity prediction model for the Finnish language attain 0.785 AUC, 0.710 balanced accuracy, and 0.723 macro-averaged F1 for predicting positive and negative polarity (binomial classification), and 0.667 AUC, 0.607 balanced accuracy, and 0.475 F1 when adding neutral tweets (multinomial classification). On the other hand, the pre-COVID-19 model trained on the same number of tweets exhibits higher accuracy for the multinomial model (0.687 balanced accuracy, and 0.588 F1). We hypothesize that this loss of performance is due to the COVID-19 context that makes sentiment analysis of neutral tweets more difficult for the machine learning algorithm to predict. Running the model on all the extracted Finnish tweets, we observe a decrease in negativity and an increase in positivity over the observed time as the Finnish government lifts restrictions. Our results show that applying an existing general-purpose sentiment analyzer on tweets that are domain-specific, such as COVID-19, provides lower accuracy. More effort in the future needs to be invested in using and developing sentiment analysis tools tailored to their application domain when conducting large-scale social media analysis of specific medical issues, such as a global pandemic.

A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data.

Health care providers and health-related researchers face significant challenges when applying sentiment analysis tools to health-related free-text survey data. Most state-of-the-art applications were developed in domains such as social media, and their performance in the health care context remains relatively unknown. Moreover, existing studies indicate that these tools often lack accuracy and produce inconsistent results. This study aims to address the lack of comparative analysis on sentiment analysis tools applied to health-related free-text survey data in the context of COVID-19. The objective was to automatically predict sentence sentiment for 2 independent COVID-19 survey data sets from the National Institutes of Health and Stanford University. Gold standard labels were created for a subset of each data set using a panel of human raters. We compared 8 state-of-the-art sentiment analysis tools on both data sets to evaluate variability and disagreement across tools. In addition, few-shot learning was explored by fine-tuning Open Pre-Trained Transformers (OPT; a large language model [LLM] with publicly available weights) using a small annotated subset and zero-shot learning using ChatGPT (an LLM without available weights). The comparison of sentiment analysis tools revealed high variability and disagreement across the evaluated tools when applied to health-related survey data. OPT and ChatGPT demonstrated superior performance, outperforming all other sentiment analysis tools. Moreover, ChatGPT outperformed OPT, exhibited higher accuracy by 6% and higher F-measure by 4% to 7%. This study demonstrates the effectiveness of LLMs, particularly the few-shot learning and zero-shot learning approaches, in the sentiment analysis of health-related survey data. These results have implications for saving human labor and improving efficiency in sentiment analysis tasks, contributing to advancements in the field of automated sentiment analysis.

A Survey of Sentiment Analysis and Sarcasm Detection

In recent years, more people have been using the internet and social media to express their opinions on various subjects, such as institutions, services, or specific ideas. This increase highlights the importance of developing automated tools for accurate sentiment analysis. Moreover, addressing sarcasm in text is crucial, as it can significantly impact the efficacy of sentiment analysis models. This paper aims to provide a comprehensive overview of the conducted research on sentiment analysis and sarcasm detection, focusing on the time from 2018 to 2023. It explores the challenges faced and the methods used to address them. It conducts a comparison of these methods. It also aims to identify emerging trends that will likely influence the future of sentiment analysis and sarcasm detection, ensuring their continued effectiveness. This paper enhances the existing knowledge by offering a comprehensive analysis of 40 research works, evaluating performance, addressing multilingual challenges, and highlighting future trends in sarcasm detection and sentiment analysis. It is a valuable resource for researchers and experts interested in the field, facilitating further advancements in sentiment analysis techniques and applications. It categorizes sentiment analysis methods into ML, lexical, and hybrid approaches, highlighting deep learning, especially Recurrent Neural Networks (RNNs), for effective textual classification with labeled or unlabeled data.

A Comparative Study of Machine Learning Models for Sentiment Analysis of Dana App Reviews

&lt;span&gt;Sentiment analysis of user reviews has become increasingly important for mobile app developers, as it can provide valuable insights into customer satisfaction and guide the improvement of app features. In this study, we compared the performance of three machine learning models - Support Vector Machine, Neural Network, and Bidirectional Long Short-Term Memory - in classifying the sentiment of user reviews for the Dana mobile application. Our results showed that the Bi-LSTM model outperformed the other models, achieving the highest accuracy, precision, recall, and F1-score. The superior performance of the Bi-LSTM model can be attributed to its ability to capture long-term dependencies and contextual information within the review text, which is crucial for accurate sentiment analysis. These findings highlight the effectiveness of deep learning techniques in handling the complexities of language and sentiment analysis, particularly in the context of user-generated content. The insights from this study can inform the development of more accurate and efficient sentiment analysis tools for mobile app reviews, ultimately benefiting both app developers and users.&lt;/span&gt;

Are Emotions Conveyed Across Machine Translations? Establishing an Analytical Process for the Effectiveness of Multilingual Sentiment Analysis with Italian Text

&#x0D; &#x0D; &#x0D; Abstract Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages.&#x0D; &#x0D; &#x0D;