Facebook Sentiment Analysis on Healthcare of Timor-Leste: Analysing public perception through social media

Goals/Purpose This study aims to examine the sentiment and polarization of social media content regarding transgender care. We seek to quantitatively analyze social media posts related to transgender care using sentiment analysis, assessing the extent of positive or negative bias in online discussions about transgender healthcare. By establishing a baseline understanding of how online misinformation may impact public perceptions of transgender care, we highlight the importance of addressing online extremism in healthcare as a public health concern. This research represents the first large-scale natural language processing (NLP) analysis of online sentiment towards transgender care, utilizing sentiment analysis to minimize bias and provide quantitative insights into the impact of online rhetoric on public perception and patient care. Methods/Technique The lead author mined X for posts that mentioned transgender surgery (TG) and gender affirming surgery (GAS) from January 2018 to December 2023. Two different NLP models then analyzed the posts. The first was a binary model that characterized each post as positive or negative. The latter was an emotion-based model that scored each post about seven emotions – fear, sadness, anger, disgust, neutral, surprise, and joy. Posts were then classified by their highest-scoring emotion. An exponential regression analysis within the Python language was performed to study the trend in annual X post volume related to gender surgery. To determine whether there were statistically significant differences in sentiment between transgender (TG) and gender-affirming surgery (GAS) topics, independent two-sample t-tests were conducted for both positive and negative sentiments for each year within the study period. Results/Complications In total, there were 81,264 posts related to gender surgery included in our analysis. In 2018 there were 2,093 posts, compared to 2023 where there were 40,258 posts – a roughly 20-fold increase in annual post volume. In our exponential regression model, annual post volume demonstrated a strong positive trend R2 = 0.939, p = 0.001, Figure 1). Most of the posts were classified as ‘negative’ (63,192 posts, 78%). Mean scores for both GAS and TG posts trended towards more negative and less positive sentiments over the study period (Figure 2). When comparing annual TG to GAS posts, those that mentioned TG were significantly more negative and less positive in every year studied (p < 0.05). Conclusion Our analysis of social media posts related to gender-affirming surgery (GAS) and transgender (TG) care reveals a significant increase in online discourse over the past five years. The volume of posts grew exponentially from 2,093 in 2018 to 40,258 in 2023, representing a 20-fold increase. This rise in online discussion reflects the growing public interest and controversy surrounding transgender healthcare.The sentiment analysis revealed a trend toward increasingly negative rhetoric. A substantial majority (78%) of the posts were classified as negative, indicating a prevalence of critical or hostile attitudes towards GAS and TG care on social media platforms. The mean sentiment scores for both GAS and TG-related posts showed a consistent shift toward more negative and less positive sentiments over time. Notably, posts specifically mentioning transgender individuals or issues consistently displayed more negative and less positive sentiments compared to general GAS-related posts across all years studied. This finding suggests that transgender-specific topics are subject to particularly intense scrutiny and criticism online.These results highlight the urgent need for strategies to address misinformation and negative rhetoric surrounding transgender healthcare on social media platforms. The increasing volume and negativity of online discussions may contribute to a hostile environment for transgender individuals seeking care and for healthcare providers offering these services. Future research should focus on developing effective interventions to counter this trend and promote more balanced, informed discussions of transgender healthcare issues online.

Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people’s hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people’s sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731–5780, 2022. https://doi.org/10.1007/s10462-022-10144-1) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people’s sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM ) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people’s reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people’s feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers.

BackgroundBreastfeeding is a critical health intervention in infants. Recent literature reported that the COVID-19 pandemic resulted in significant mental health issues in pregnant and breastfeeding women due to social isolation and lack of direct professional support. These maternal mental health issues affected infant nutrition and decreased breastfeeding rates during COVID-19. Twitter, a popular social media platform, can provide insight into public perceptions and sentiment about various health-related topics. With evidence of significant mental health issues among women during the COVID-19 pandemic, the perception of infant nutrition, specifically breastfeeding, remains unknown.MethodsWe aimed to understand public perceptions and sentiment regarding breastfeeding during the COVID-19 pandemic through Twitter analysis using natural language processing techniques. We collected and analyzed tweets related to breastfeeding and COVID-19 during the pandemic from January 2020 to May 2022. We used Python software (v3.9.0) for all data processing and analyses. We performed sentiment and emotion analysis of the tweets using natural language processing libraries and topic modeling using an unsupervised machine-learning algorithm.ResultsWe analyzed 40,628 tweets related to breastfeeding and COVID-19 generated by 28,216 users. Emotion analysis revealed predominantly “Positive emotions” regarding breastfeeding, comprising 72% of tweets. The overall tweet sentiment was positive, with a mean weekly sentiment of 0.25 throughout, and was affected by external events. Topic modeling revealed six significant themes related to breastfeeding and COVID-19. Passive immunity through breastfeeding after maternal vaccination had the highest mean positive sentiment score of 0.32.ConclusionsOur study provides insight into public perceptions and sentiment regarding breastfeeding during the COVID-19 pandemic. Contrary to other topics we explored in the context of COVID (e.g., ivermectin, disinformation), we found that breastfeeding had an overall positive sentiment during the pandemic despite the documented rise in mental health challenges in pregnant and breastfeeding mothers. The wide range of topics on Twitter related to breastfeeding provides an opportunity for active engagement by the medical community and timely dissemination of advice, support, and guidance. Future studies should leverage social media analysis to gain real-time insight into public health topics of importance in child health and apply targeted interventions.

Internet-based platforms such as social media have a great deal of big data that is available in the shape of text, audio, video, and image. Sentiment Analysis (SA) of this big data has become a field of computational studies. Therefore, SA is necessary in texts in the form of messages or posts to determine whether a sentiment is negative or positive. SA is also crucial for the development of opinion mining systems. SA combines techniques of Natural Language Processing (NLP) with data mining approaches for developing inelegant systems. Therefore, an approach that can classify sentiments into two classes, namely, positive sentiment and negative sentiment is proposed. A Multilayer Perceptron (MLP) classifier has been used in this document classification system. The present research aims to provide an effective approach to improving the accuracy of SA systems. The proposed approach is applied to and tested on two datasets, namely, a Twitter dataset and a movie review dataset; the accuracies achieved reach 85% and 99% respectively.

Due to the growing volume of available textual information, there is a great demand for Natural Language Processing (NLP) techniques that can automatically process and manage texts, supporting the information retrieval and communication in core areas of society (e.g. healthcare, business, and science). NLP techniques have to tackle the often ambiguous and linguistic structures that people use in everyday speech. As such, there are many issues that have to be considered, for instance slang, grammatical errors, regional dialects, figurative language , etc. Figurative Language (FL), such as irony , sarcasm , simile, and metaphor, poses a serious challenge to NLP systems. FL is a frequent phenomenon within human communication, occurring both in spoken and written discourse including books, websites, fora, chats, social network posts, news articles and product reviews. Indeed, knowing what people think can help companies, political parties, and other public entities in strategizing and decision-making polices. When people are engaged in an informal conversation, they almost inevitably use irony (or sarcasm) to express something else or different than stated by the literal sentence meaning. Sentiment analysis methods can be easily misled by the presence of words that have a strong polarity but are used sarcastically, which means that the opposite polarity was intended. Several efforts have been recently devoted to detect and tackle FL phenomena in social media. Many of applications rely on task-specific lexicons (e.g. dictionaries, word classifications) or Machine Learning algorithms. Increasingly, numerous companies have begun to leverage automated methods for inferring consumer sentiment from online reviews and other sources. A system capable of interpreting FL would be extremely beneficial to a wide range of practical NLP applications. In this sense, this chapter aims at evaluating how two specific domains of FL, sarcasm and irony, affect Sentiment Analysis (SA) tools. The study’s ultimate goal is to find out if FL hinders the performance (polarity detection) of SA systems due to the presence of ironic context. Our results indicate that computational intelligence approaches are more suitable in presence of irony and sarcasm in Twitter classification.

BackgroundUnstructured patient feedback (UPF) allows patients to freely express their experiences without the constraints of predefined questions. The proliferation of online health care rating websites has created a vast source of UPF. Natural language processing (NLP) techniques, particularly sentiment analysis and topic modeling, are increasingly being used to analyze UPF in health care settings; however, the scope and clinical relevance of these technologies are unclear.ObjectiveThis scoping review investigates how NLP techniques are being used to interpret UPF, with a focus on the health care settings in which this is used, the purposes for using these technologies, and any impacts reported on clinical practice.MethodsSearches of the MEDLINE, Embase, CINAHL, Cochrane Database of Reviews, and Google Scholar were conducted in February 2024. No date limits were applied. Eligibility criteria included English-language studies that used NLP techniques on UPF that pertained to an identifiable health care setting or providers. Studies were excluded if human actors solely performed coding or if NLP was applied to structured feedback or non–patient-generated content. Data were extracted and narratively synthesized regarding health care settings, NLP methods, and clinical applications.ResultsFrom 4017 records, 52 studies met inclusion criteria. NLP was most commonly applied to UPF from secondary care settings (n=33) with fewer in primary (n=10) or community (n=5) care. Three NLP techniques were identified in the included studies: sentiment analysis (n=32), topic modeling (n=15), and text classification (n=7). Sentiment analysis was applied to explore associations between patient sentiment and health care provider characteristics, track emotional responses over time, and identify areas for improvement in health care delivery. Topic modeling, primarily using latent Dirichlet allocation algorithm, was used to uncover latent themes in patient feedback, compare patient experiences across different health care settings, and track changes in patient concerns over time. Text classification was used to categorize patient feedback into predefined topics. The association between NLP-derived insights and traditional health care quality metrics was limited, with few studies describing concrete clinical impacts resulting from their analyses.ConclusionsNLP has been applied to UPF across a number of contexts, primarily to identify features of health services or professionals that support good patient experience. The growth of research publications demonstrates an academic interest in these technologies, but there is little evidence these approaches are being used in clinical settings. Future research is required to assess how NLP may capture the nuance of health care interactions, align with existing quality metrics, and how it may be used to influence clinician behavior.

BACKGROUND Unstructured patient feedback (UPF) allows patients to freely express their experiences without the constraints of predefined questions. The proliferation of online healthcare rating websites has created a vast source of UPF. Natural language processing (NLP) techniques, particularly sentiment analysis and topic modelling, are increasingly being used to analyse UPF in healthcare settings, however the scope and clinical relevance of these technologies is unclear. OBJECTIVE This scoping review investigates how NLP techniques are being used to interpret UPF, with focus on the healthcare settings in which this is used, the purposes for using these technologies, and any impacts reported on clinical practice. METHODS Searches of the MEDLINE, EMBASE, CINAHL, Cochrane Database of Reviews, and Google Scholar were conducted in February 2024. No date limits were applied. English language studies that used NLP techniques on UPF that pertained to an identifiable health care setting or provider were included. Data extraction focused on the healthcare setting, NLP methods used, and applications of these techniques. RESULTS 52 studies were included. NLP was most commonly applied to UPF from secondary care settings (n=33) with fewer in primary (n=10) or community (n=5) care. Three NLP techniques were identified in the included studies: sentiment analysis (n=32), topic modelling (n=15) and text classification (n=7). Sentiment analysis was applied to explore associations between patient sentiment and healthcare provider characteristics, track emotional responses over time, and identify areas for improvement in healthcare delivery. Topic modelling, primarily using Latent Dirichlet Allocation (LDA) algorithm, was employed to uncover latent themes in patient feedback, compare patient experiences across different healthcare settings, and track changes in patient concerns over time. Text classification was used to categorize patient feedback into predefined topics. The association between NLP-derived insights and traditional healthcare quality metrics was limited, with few studies describing concrete clinical impacts resulting from their analyses. CONCLUSIONS NLP has been applied to UPF across a number of contexts, primarily to identify features of health services or professionals that support good patient experience. The growth of research publications demonstrates an academic interest in these technologies, but there is little evidence these approaches are being employed in clinical settings. Future research is required to assess how NLP may capture the nuance of healthcare interactions, align with existing quality metrics and how it may be used to influence clinician behaviour

BACKGROUND Depression, a highly prevalent global mental disorder, has prompted significant research concerning its association with social media use and its impact during Hong Kong’s social unrest and COVID-19 pandemic. However, other mainstream media, specifically online news, has been largely overlooked. Despite extensive research conducted in countries, such as the United States, Australia, and Canada, to investigate the latent subthemes, sentiments, and coping strategies portrayed in depression-related news, the landscape in Hong Kong remains unexplored. OBJECTIVE This study aims to uncover the latent subthemes presented in the online news coverage of depression in Hong Kong, examine the sentiment conveyed in the news, and assess whether coping strategies have been provided in the news for individuals experiencing depression. METHODS This study used natural language processing (NLP) techniques, namely the latent Dirichlet allocation topic modeling and the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiment analysis, to fulfill the first and second objectives. Coping strategies were rigorously assessed and manually labeled with designated categories by content analysis. The online news was collected from February 2019 to May 2024 from Hong Kong mainstream news websites to examine the latest portrayal of depression, particularly during and after the social unrest and the COVID-19 pandemic. RESULTS In total, 2435 news articles were retained for data analysis after the news screening process. A total of 7 subthemes were identified based on the topic modeling results. <i>Societal system</i>, <i>law enforcement</i>, <i>global recession</i>, <i>lifestyle</i>, <i>leisure</i>, <i>health issues</i>, and <i>US politics</i> were the latent subthemes. Moreover, the overall news exhibited a slightly positive sentiment. The correlations between the sentiment scores and the latent subthemes indicated that the societal system, law enforcement, health issues, and US politics revealed negative tendencies, while the remainder leaned toward a positive sentiment. The coping strategies for depression were substantially lacking; however, the categories emphasizing <i>information on skills and resources</i> and <i>individual adjustment</i> to cope with depression emerged as the priority focus. CONCLUSIONS This pioneering study used a mixed methods approach where NLP was used to investigate latent subthemes and underlying sentiment in online news. Content analysis was also performed to examine available coping strategies. The findings of this research enhance our understanding of how depression is portrayed through online news in Hong Kong and the preferable coping strategies being used to mitigate depression. The potential impact on readers was discussed. Future research is encouraged to address the mentioned implications and limitations, with recommendations to apply advanced NLP techniques to a new mental health issue case or language.

Depression, a highly prevalent global mental disorder, has prompted significant research concerning its association with social media use and its impact during Hong Kong's social unrest and COVID-19 pandemic. However, other mainstream media, specifically online news, has been largely overlooked. Despite extensive research conducted in countries, such as the United States, Australia, and Canada, to investigate the latent subthemes, sentiments, and coping strategies portrayed in depression-related news, the landscape in Hong Kong remains unexplored. This study aims to uncover the latent subthemes presented in the online news coverage of depression in Hong Kong, examine the sentiment conveyed in the news, and assess whether coping strategies have been provided in the news for individuals experiencing depression. This study used natural language processing (NLP) techniques, namely the latent Dirichlet allocation topic modeling and the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiment analysis, to fulfill the first and second objectives. Coping strategies were rigorously assessed and manually labeled with designated categories by content analysis. The online news was collected from February 2019 to May 2024 from Hong Kong mainstream news websites to examine the latest portrayal of depression, particularly during and after the social unrest and the COVID-19 pandemic. In total, 2435 news articles were retained for data analysis after the news screening process. A total of 7 subthemes were identified based on the topic modeling results. Societal system, law enforcement, global recession, lifestyle, leisure, health issues, and US politics were the latent subthemes. Moreover, the overall news exhibited a slightly positive sentiment. The correlations between the sentiment scores and the latent subthemes indicated that the societal system, law enforcement, health issues, and US politics revealed negative tendencies, while the remainder leaned toward a positive sentiment. The coping strategies for depression were substantially lacking; however, the categories emphasizing information on skills and resources and individual adjustment to cope with depression emerged as the priority focus. This pioneering study used a mixed methods approach where NLP was used to investigate latent subthemes and underlying sentiment in online news. Content analysis was also performed to examine available coping strategies. The findings of this research enhance our understanding of how depression is portrayed through online news in Hong Kong and the preferable coping strategies being used to mitigate depression. The potential impact on readers was discussed. Future research is encouraged to address the mentioned implications and limitations, with recommendations to apply advanced NLP techniques to a new mental health issue case or language.

Aim: This scoping review aimed to identify and synthesize the evidence in existing nursing studies that used natural language processing to analyze social media data, and the relevant procedures, techniques, tools, and ethical issues. Background: Social media has widely integrated into both everyday life and the nursing profession, resulting in the accumulation of extensive nursing-related social media data. The analysis of such data facilitates the generation of evidence thereby aiding in the formation of better policies. Natural language processing has emerged as a promising methodology for analyzing social media data in the field of nursing. However, the extent of natural language processing applications in analyzing nursing-related social media data remains unknown. Evaluation: A scoping review was conducted. PubMed, CINAHL, Web of Science and IEEE Xplore were searched. Studies were screened based on inclusion criteria. Relevant data were extracted and summarized using a descriptive approach. Key Issues: In total, 38 studies were included for the final analysis. Topic modeling and sentiment analysis were the most frequently employed natural language processing techniques. The most used topic modeling algorithm was latent Dirichlet allocation. The dictionary-based approach was the most utilized sentiment analysis approach, and the National Research Council Sentiment and Emotion Lexicons was the most used sentiment dictionary. Natural language processing tools such as Python (NLTK, Jieba, spaCy, and KoNLP library) and R (LDAvis, Jaccard, ldatuning, and SentiWordNet packages) were documented. A significant proportion of the included studies did not obtain ethical approval and did not conduct data anonymization on social media users' information. Conclusion: This scoping review summarized the extent of natural language processing techniques adoption in nursing and relevant procedures and tools, offering valuable resources for researchers who are interested in discovering knowledge from social media data. The study also highlighted that the application of natural language processing for analyzing nursing-related social media data is still emerging, indicating opportunities for future methodological improvements. Implications for Nursing Management: There is a need for a standardized management framework for conducting and reporting studies using natural language processing techniques in the analysis of nursing-related social media data. The findings could inform the development of regulatory policies by nursing authorities.

This study analyzed perceptions of Indians regarding COVID-19 booster dose vaccines using natural language processing techniques, particularly, sentiment analysis and topic modeling. We analyzed tweets generated by Indian citizens for this study. In late July 2022, the Indian government hastened the process of COVID-19 booster dose vaccinations. Understanding the emotions and concerns of the citizens regarding the health policy being implemented will assist the government, health policy officials, and policymakers implement the policy efficiently so that desired results can be achieved. Seventy-six thousand nine hundred seventy-nine tweets were used for this study. The sentiment analysis study revealed that out of those 76,979 tweets, more than half (n = 40,719 tweets (52.8%) had negative sentiments, 24,242 tweets (31.5%) had neutral sentiments, and 12,018 tweets (15.6%) had positive sentiments. Social media posts by Indians on the COVID-19 booster doses have focused on the feelings that younger people do not need vaccines and that vaccinations are unhealthy.

PurposeStudent recruitment and retention are important issues for all higher education institutions. Constant monitoring of student satisfaction levels is therefore crucial. Traditionally, students voice their opinions through official surveys organized by the universities. In addition to that, nowadays, social media and review websites such as “Rate my professors” are rich sources of opinions that should not be ignored. Automated mining of students’ opinions can be realized via aspect-based sentiment analysis (ABSA). ABSA s is a sub-discipline of natural language processing (NLP) that focusses on the identification of sentiments (negative, neutral, positive) and aspects (sentiment targets) in a sentence. The purpose of this paper is to introduce a system for ABSA of free text reviews expressed in student opinion surveys in the Serbian language. Sentiment analysis was carried out at the finest level of text granularity – the level of sentence segment (phrase and clause).Design/methodology/approachThe presented system relies on NLP techniques, machine learning models, rules and dictionaries. The corpora collected and annotated for system development and evaluation comprise students’ reviews of teaching staff at the Faculty of Technical Sciences, University of Novi Sad, Serbia, and a corpus of publicly available reviews from the Serbian equivalent of the “Rate my professors” website.FindingsThe research results indicate that positive sentiment can successfully be identified with the F-measure of 0.83, while negative sentiment can be detected with the F-measure of 0.94. While the F-measure for the aspect’s range is between 0.49 and 0.89, depending on their frequency in the corpus. Furthermore, the authors have concluded that the quality of ABSA depends on the source of the reviews (official students’ surveys vs review websites).Practical implicationsThe system for ABSA presented in this paper could improve the quality of service provided by the Serbian higher education institutions through a more effective search and summary of students’ opinions. For example, a particular educational institution could very easily find out which aspects of their service the students are not satisfied with and to which aspects of their service more attention should be directed.Originality/valueTo the best of the authors’ knowledge, this is the first study of ABSA carried out at the level of sentence segment for the Serbian language. The methodology and findings presented in this paper provide a much-needed bases for further work on sentiment analysis for the Serbian language that is well under-resourced and under-researched in this area.

In modern age of information explosion, every day millions of gigabytes of data are generated in the form of documents, web pages, e-mail, social media text, blogs etc., so importance of effective and efficient Natural Language Processing techniques become crucial for an information retrieval system, text summarization, sentiment analysis, information extraction, named entity recognition, relationship extraction, social media monitoring, text mining, language translation program, and question answering system. Natural Language Processing is a computational technique applies different levels of linguistic analysis for representing natural language into a useful representation for further processing. NLP is recognized as a challenging task in computer science and artificial intelligence because understanding human natural language is not only depends on the words but how those words are linked together to form precise meaning is also considered. Regardless of language being one of the easiest concepts for human to learn, but for training computers to understand natural language is a difficult task due to the ambiguity of language syntax and semantics. Natural Language processing techniques involves processing documents or text which reduces storage space and also reduces the size of index and understanding the given information which satisfies user’s need. NLP techniques improve the performance of the information retrieval efficiency and effective documentation processes. Common dialect handling procedures incorporates tokenization, stop word expulsion, stemming, lemmatization, parts of discourse labeling, lumping and named substance recognizer which enhances execution of NLP applications. The Natural Language Toolkit is the best possible solution for learning the ropes of NLP domain. NLTK, a collection of application packages which encourage researchers and learners in natural language processing, computational linguistics and artificial intelligence.

Sentiment analysis is widely used to identify emotions and attitudes in text. With the growing popularity of audio-based social platforms and the significant rise in spoken data, sentiment analysis in the auditory domain has become increasingly important. This paper explores sentiment analysis in audio data using Natural Language Processing (NLP) techniques. We propose a novel method for extracting linguistic features and developing emotional models tailored to audio-based sentiment. In our experiments, we compare deep learning models with traditional NLP techniques, using a unique dataset to validate our findings. Sentiment analysis, also known as opinion mining, is a key subfield of NLP, focusing on extracting subjective information from textual data. The surge of user-generated content on online platforms like social media, blogs, and product reviews has amplified the importance of sentiment analysis for understanding public opinion and consumer behavior. This paper provides an overview of various approaches used in sentiment analysis, including machine learning, lexicon-based methods, and deep learning, highlighting their strengths and limitations. We discuss the trade-offs between accuracy, computational efficiency, and interpretability for each approach, while addressing challenges like sarcasm detection, context dependency, and domain-specific language. Additionally, we examine recent advancements in the field, such as the use of cross-sectional models and the integration of multiple data sources to provide a more comprehensive view of sentiment. Our results demonstrate the efficiency and high performance of the proposed models in capturing sentiment from audio data. The study also explores the ethical considerations, practical applications, and broader relevance of audio-based sentiment analysis across media and other domains. Finally, we conclude by discussing future directions, emphasizing the need for more robust models capable of handling diverse and complex data, along with ethical considerations for real-world applications.

소셜미디어 콘텐츠의 오피니언 마이닝결과 시각화: N라면 사례 분석 연구