NLTK Research Articles

Classification of Banjarese Hulu and Kuala Dialects in Banjarese Prose Texts

This research focuses on classifying the Hulu and Kuala Banjarese dialects in the prose text “Datu Kandangan and Datu Kartamina”. These dialects represent linguistic variations resulting from geographical, social, and cultural differences among language communities, particularly in South Kalimantan, Indonesia. Language analysis methods such as Python Natural Language Toolkit (NLTK), NumPy, and Latent Dirichlet Allocation (LDA) Visualization (LyLDAvis) were employed to classify the dialects, involving data preprocessing steps like tokenization, punctuation removal, stop word normalization, and stemming. The research findings reveal the superiority of the "Naive Bayes" method over the "Boolean Query," achieving high accuracy in identifying positive examples and classifying texts into Upper and Lower Banjar dialects. The "Naive Bayes" method outperforms the "Boolean Query" with precision and recall values of 0.955563 and 0.956098, while the "Boolean Query" only reaches 0.021416 and 0.146341. This study makes a significant scholarly contribution to understanding language and cultural diversity in South Kalimantan, opening opportunities for further exploration in developing Natural Language Processing (NLP) technology for Indonesian regional languages.

Disinformation, Fakes and Propaganda Identifying Methods in Online Messages Based on NLP and Machine Learning Methods

A new method of propaganda analysis is proposed to identify signs and change the dynamics of the behaviour of coordinated groups based on machine learning at the processing disinformation stages. In the course of the work, two models were implemented to recognise propaganda in textual data - at the message level and the phrase level. Within the framework of solving the problem of analysis and recognition of text data, in particular, fake news on the Internet, an important component of NLP technology (natural language processing) is the classification of words in text data. In this context, classification is the assignment or assignment of textual data to one or more predefined categories or classes. For this purpose, the task of binary text classification was solved. Both models are built based on logistic regression, and in the process of data preparation and feature extraction, such methods as vectorisation using TF-IDF vectorisation (Term Frequency – Inverse Document Frequency), the BOW model (Bag-of-Words), POS marking (Part-Of-Speech), word embedding using the Word2Vec two-layer neural network, as well as manual feature extraction methods aimed at identifying specific methods of political propaganda in texts are used. The analogues of the project under development are analysed the subject area (the propaganda used in the media and the basis of its production methods) is studied. The software implementation is carried out in Python, using the seaborn, matplotlib, genism, spacy, NLTK (Natural Language Toolkit), NumPy, pandas, scikit-learn libraries. The model's score for propaganda recognition at the phrase level was obtained: 0.74, and at the message level: 0.99. The implementation of the results will significantly reduce the time required to make the most appropriate decision on the implementation of counter-disinformation measures concerning the identified coordinated groups of disinformation generation, fake news and propaganda. Different classification algorithms for detecting fake news and non-fakes or fakes identification accuracy from Internet resources ana social mass media are used as the decision tree (for non-fakes identification accuracy 0.98 and fakes identification accuracy 0.9903), the k-nearest neighbours (0.83/0.999), the random forest (0.991/0.933), the multilayer perceptron (0.9979/0.9945), the logistic regression (0.9965/0.9988), and the Bayes classifier (0.998/0.913). The logistic regression (0.9965) the multilayer perceptron (0.9979) and the Bayesian classifier (0.998) are more optimal for non-fakes news identification. The logistic regression (0.9988), the multilayer perceptron (0.9945), and k-nearest neighbours (0.999) are more optimal for identifying fake news identification.

Decoding prognosis: Unraveling physicians' responses in electronic treatment plans.

230 Background: Communicating prognosis to cancer patients is a critical aspect of shared decision making in healthcare. Practices in The US Oncology Network (The Network) use an electronic version of a treatment plan based on the Institute of Medicine (IOM) care management plan for patients receiving cancer treatment. Physicians document in their own words the prognosis section of the treatment plan. This study seeks to better understand the responses provided by physicians in the prognosis section. Methods: We evaluated word frequency analysis using FreqDist library, conducted sentiment analyses using the Natural Language Toolkit (NLTK) Sentiment Intensity Analyzer, and performed thematic analyses using Latent Dirichlet Allocation library on a random sample of 35,000 treatment plans developed in 2023 for practices in The Network. Results: 32% of the responses were 5 words or less, with 3.8% single word responses. Words like “excellent, good, fair, poor, guarded” or a combination thereof were commonly used to describe the prognosis. 26% of responses were ≥12 words in length, and generally were personalized to the patient’s disease, treatment, and/or contained situation specific narratives. Responses <12 words generally contained templated phrases, or minimally customized responses. 38% of the responses contained a numerical value, representing either a percentage probability of survival, chance of response to treatment, or a quantification of time (months, years). Thematic analyses conducted on responses ≥12 words revealed that the predominant theme focused on survival represented in different ways, followed by response to therapy, probability of recurrence or relapse, and disease control. Sentiment analyses revealed that 60.6% of responses denoted a negative sentiment. 71.7% of responses ≤5 words in length had a negative sentiment, compared to 58.3% of responses ≥12 words in length. Conclusions: This study reveals that prognosis responses from physicians used on treatment plans shared with patients are very diverse. The manner in which prognosis is presented to patients can greatly influence their understanding, engagement, and involvement in making treatment decisions. To optimize shared decision making in cancer care, healthcare providers must strive to effectively communicate prognosis by using clear and empathetic language, ensuring patient comprehension, and tailoring the information to individual patient needs and preferences. The data set has high potential for further study to better understand the role and impact of written treatment plans to help physicians refine this documentation for better patient understanding.

WP6.6 - Sentiment Analysis of Social Media Posts by Medical Professional in the UK

Abstract Aims Notoriously described as being controversial in nature, we aim to objectively assess the language used amongst medical professionals in social media (SM). Methods Data was extracted from the Reddit SM website under the sub-page "r/DoctorsUK" forum, through a data-extraction code written in the python language. This was then analysed through a Natural Language Toolkit algorithm to classify language used into positive, negative and neutral sentiments with an overall polarity score ranging from -1 noting most negative to 1 being most positive. Results A total of 992 articles were examined over a 24-day period, as limited by Reddit data application programme interface (API) protocols. A total of 210 positive, 218 negative and 564 neutral articles were noted with an average of 43 articles posted per day. Headlines sentiment on average was overall marginally positive at 0.0154. Comment tree within each headline was also examined, noting a more positive 0.173. Through regression analysis, it was noted that headline and comment sentiments was directly correlated, and this was statistically significant(P=1.24*10-10). It was also noted that more negative headlines had higher “likes” and were therefore featured more prominently within the forum. Conclusions Although SM posts are overall positive in sentiment, it is noted that more negative sentiments are often up-voted and therefore featured more prominently due to their more provocative nature and emotions incited amongst readers. As SM posts elicits strong psychological and psychosocial reactions from a community, there is an urgency to more accurate data driven models in developing future policies to maintain an effective professional morale within the NHS.

Sentiment Analysis of Product Reviews using Rule-based and Deep-Learning Models

This study analyses the feelings and opinions expressed in product reviews by utilizing NLTK (Natural Language Toolkit) and deep learning models to forecast customer sentiments and evaluate the probability of product purchases based on the review information available. The study examines the sentiment analysis applied to the Flipkart product reviews using a sentiment dataset. The NLTK, VADER, and RoBERTa models are evaluated for their effectiveness in predicting the sentiment of the customers. The analysis shows that the VADER, as a rule-based model is efficient in handling short and simple reviews but faces challenges with more complex sentiments. Meanwhile, RoBERTa outperforms VADER, with a Mean Absolute Error (MAE) of 0.12 and an R2 value of 0.85. The comparative study shows the ability of RoBERTa to capture subtle emotions in customer reviews and accurately understand customer feedback, proving to be valuable in e-commerce for optimizing product recommendations and customer satisfaction.

Expression and Evolution of Public Opinion in Social Network VKontakte During COVID-19 Pandemic

This article analyzes comments on the social network VKontakte during the COVID-19 pandemic with the aim of uncovering public opinion and sentiments. A discursive model of comments on official materials related to the pandemic was constructed using the qualitative data analysis software tool Nvivo12. The Natural Language Toolkit (NLTK) was employed for this purpose. As a result of a comprehensive analysis of the evolution of "hot spots" of discussion, it was found that during the pandemic, comments in the VKontakte group "StopCoronavirus.rf" addressed several thematic directions: "political measures for pandemic prevention," "protection of the civilian population from the pandemic," "medical prevention of virus spread," "the pandemic and people's livelihoods," "credibility of epidemiological data," "pandemic trends," and "spiritual support and encouragement." Among these, users expressed the greatest concern regarding subtopics related to people's behavior in preventing the virus's spread, the veracity of government-released data, the scale of testing, as well as the reliability of vaccines, online education, and stress caused by the pandemic. As the pandemic progressed, the frequency of discussions on specific topics fluctuated. Negative sentiments within society exhibited a tendency to rise, then decrease, and subsequently rise again, mirroring the overall trends of increasing and decreasing morbidity rates across Russia.

Exploring mechanobiology network of bone and dental tissue based on Natural Language Processing

Bone and cartilage tissues are physiologically dynamic organs that are systematically regulated by mechanical inputs. At cellular level, mechanical stimulation engages an intricate network where mechano-sensors and transmitters cooperate to manipulate downstream signaling. Despite accumulating evidence, there is a notable underutilization of available information, due to limited integration and analysis. In this context, we conceived an interactive web tool named MechanoBone to introduce a new avenue of literature-based discovery. Initially, we compiled a literature database by sourcing content from Pubmed and processing it through the Natural Language Toolkit project, Pubtator, and a custom library. We identified direct co-occurrence among entities based on existing evidence, archiving in a relational database via SQLite. Latent connections were then quantified by leveraging the Link Prediction algorithm. Secondly, mechanobiological pathway maps were generated, and an entity-pathway correlation scoring system was established through weighted algorithm based on our database, String, and KEGG, predicting potential functions of specific entities. Additionally, we established a mechanical circumstance-based exploration to sort genes by their relevance based on big data, revealing the potential mechanically sensitive factors in bone research and future clinical applications. In conclusion, MechanoBone enables: 1) interpreting mechanobiological processes; 2) identifying correlations and crosstalk among molecules and pathways under specific mechanical conditions; 3) connecting clinical applications with mechanobiological processes in bone research. It offers a literature mining tool with visualization and interactivity, facilitating targeted molecule navigation and prediction within the mechanobiological framework of bone-related cells, thereby enhancing knowledge sharing and big data analysis in the biomedical realm.

Exploring new frontiers of experiential learning landscape: a hybrid review

PurposeExperiential learning is crucial in education, as it offers hands-on, practical experiences that enable individuals to develop their skills and knowledge more engagingly and interactively. In recent years, experiential learning has become a significant aspect of education. To provide academic scholars with a thorough roadmap for further investigation, this study aims to provide useful insights into the bibliometric and content analysis of experiential learning, including keywords, well-known authors, publications, nations and topics.Design/methodology/approachThis research does a rigorous bibliometric analysis to give a thorough and visually instructional assessment of the evolution and advancement of the literature on experiential learning. Its fast development between 1976 and 2022 is meticulously tracked in the research. By using VOSviewer and Biblioshiny tools, the present study presents a concise overview of 507 records retrieved from the Scopus database using the keyword “Experiential Learning”, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol. Deeper text mining was done using Python libraries “Pandas” and “Natural Language Toolkit” and regular expressions.FindingsThe findings reveal a surge in the number of publications on experiential learning and provide insights, particularly using the theory, context, characteristics, methodology analysis, supporting researchers and practitioners to understand learning better and provide perspectives for future research. Descriptive bibliometric analysis showed that most contributions are from the USA, the UK and Canada. In-depth content analysis revealed five clusters: developments in learning, management education, engineering curricula, organisational learning and knowledge management and entrepreneurship education. The keyword co-occurrence analysis enabled linkages between relevant fields of study and significant research domains. The most commonly used theories were: experiential learning theory, social learning theory, relational coordination theory, empowerment theory, feedback learning theory, effectuation theory and human capital theory.Originality/valueThis study uses information from the Scopus database to do a bibliometric analysis of experiential learning from 1976 to 2022. This study serves as a valuable resource for researchers in the field, helping them to position their work more explicitly within the existing literature and highlighting potential areas for future research. It does this by thoroughly analysing the literature on experiential learning using bibliometrics.

Алгоритмы поиска вербальных маркеров идентичности в современном научном дискурсе

The article is devoted to the study of identity verbalization specifics via Data Mining. The research material consists of English texts from Internet scientific repositories and e-libraries devoted to various concepts of youth identity. A methodology based on the use of modern natural language processing and machine learning tools was developed and tested as part of the research. The analysis was carried out using the Natural Language Toolkit library for tokenization and POS-tagging procedures for calculating the frequency of tokens from the «identity» environment. Word Embeddings, pre-trained Word2Vec model and K-means algorithm were used for the subsequent analysis and clustering of words based on their semantic proximity. Gensim library and Scikit-learn library were used to work with the Word2Vec model. As a result, it was proved that in English scientific discourse young person’s identity is verbalized within 9 semantic categories: behavior, communities, communication, education, identity, language, practice, complexity, science, the most common of which are education (33%), language (21%) and communities (18%). N-grams analysis made it possible to identify semantic fields, establish their attributes, and evaluate texts’ similarity, which provided the most accurate vector space search for semantically close n-grams. Optimization made it possible to establish a similarity measure to rank phrases according to the query, as well as assign each n-gram a certain ranking weight. Improvements can be achieved by adding statistical word weighting, such as TF-IDF. The proposed system is capable of searching in a large text array of related phrases with a similar meaning.

Getting a handle on a Hansard with Python and NLTK, or how to tame the linguistic picture of British politics with NLP

The article proposes an optimised starter’s set of basic Python and NLTK (Natural Language Toolkit) methods that are essential in the analysis of massive textual corpora conducted as part of research investigating linguistic images of the world. The need to specify and detail these applied techniques stems from the nature and scope of the inexorable challenges confronted by contemporary cognitive linguistics and lexicology in the realm of unstructured big data analysis. Their viability and practical value are demonstrated in a series of illustrative examples where they are applied to the processing of continuous parallel diachronic corpora of Hansard that capture the discourse of both chambers of the British parliament produced in the years 2006-2023 and jointly amounting to over a third of a billion tokens.The article suggests that the methods it outlines and classifies can be seen as forming an indispensable minimum of IT competences that is capable of delivering a substantial boost to the level of research both as regards its overall quality and its competitive edge. The proposed toolkit includes an essential set of instruments for target vocabulary processing as well as for the assessment and visualisation of word and phrase frequency and collocation.The author presumes that, urged by the need to keep abreast of prevailing trends, the contemporary Russian researcher of linguistic images of the world is highly likely to find themselves compelled at some point to embrace the quantitative analysis methods made possible by combining Python and NLTK. As part of its substantial and varied range of benefits, the latter would arguably help them design and customise research protocols, adapting them with ease and versatility. Lastly and most importantly, the author suggests that Python and NLTK skills may serve as a comfortable gateway towards eventually upgrading one’s linguistic research to cutting-edge global standards of technological sophistication and marketability.

Diagnosis of Schizophrenia and Psychosis in Māori People Using Speech Assay: A Natural Language Processing Approach

Indigenous peoples and ethnic minorities worldwide have a higher incidence of psychosis, primarily schizophrenia. In New Zealand, Māori individuals experience greater rates of anxiety and depression compared to non-Māori, with a significantly higher incidence of mental illness among Māori. We propose natural language processing (NLP) trained on speech samples from Māori patients as a potential solution to the problem of culturally biased psychometric screening tools for psychosis and schizophrenia. This research examines NLP's ability to diagnose psychosis in Māori patients by analysing speech and language abnormalities as indicators of severe mental illnesses such as schizophrenia. Our research emphasizes the need for inclusive language models and investigates cross-cultural applicability. We employed a three-part method: conducting clinical interviews, pre-processing data with the Natural Language Toolkit (NLTK) and applying language classifiers. The study's results demonstrate the promise of NLP, but limited patient data necessitates further research, including standardizing datasets and integrating NLP with indigenous languages. This research represents a step towards improving diagnostic accuracy and support for Māori people suffering from psychosis, aligning with healthcare's goal of fair and culturally responsive mental health screening.

The Impact of the EU AI Act on the UN Sustainable Development Goals for 2030 – A Text Analysis

Abstract This article conducts an analysis on the content of the Artificial Intelligence Act which has recently been passed by the European Parliament and which is expected to be implemented during a two-year stepped transition period. Artificial Intelligence has been praised for significantly increasing the efficiency with which businesses, institutions, and countries as an extension, have been able to develop their sustainable development goal scores towards the United Nations’ 2030 ambitions, especially in implementing sustainable business development, urban development, renewable energy, healthcare, education and more. Contrary to these contributions, a large number of researchers and international organizations have signaled the necessity of ensuring the application of ethical and legal considerations during the development and usage of artificial intelligence and machine learning models through the use of data management, protection, and ethical modeling experts within organizations. The article thus seeks to highlight the areas of sustainable development most impacted by the introduction of the Artificial Intelligence Act, both negatively in slowing down innovation, and positively in ensuring the safe and ethical use of these technologies. The methodology of research used involves sentiment analysis using Python’s Natural Language Toolkit followed by keyword mapping to enable a targeted view into the most positive/negative pages of the act, the most highly mentioned sustainable goals, and the mapping between this sentiment and the mention count.

Implementation of Text Processing Techniques on Citizen Opinions Regarding Floods in Surabaya

An analysis of residents opinions on flooding in Surabaya is essential to identify their perceptions and aspirations towards this disaster. This can facilitate the making of appropriate flood mitigation policies. The current problem is that it is difficult to retrieve data from X in the form of both users and tweets automatically. So that in some studies that use tweet data becomes less efficient in the data collection process. This research confirmed the use of messages to determine highly impactful disaster zones and showed how tweets can be used to identify oscillations in disaster intensity over time. The topic of this research is to apply the Data Crawling method to obtain datasets on social media X. Then the next method is text preprocessing using Wordcloud, Matplotlib, (NTLK) Natural Language Toolkit, and Sastrawi libraries. In natural language processing, the data to be extracted includes unstructured or “arbitrary” data. In normal dialect preparing (NLP), the information to be extracted includes unstructured or "self-assertive" information. For future purposes (assumption examination, subject modeling, etc.), such information must be changed over into organized data. The discoveries of the think about can help the organization in comprehending the necessities and inclinations of the people with respect to surges. This article demonstrates how Artificial Intelegence may be applied to text data analysis in order to provide insightful findings. the outcomes of this research can help the government in making more effective policies to overcome flooding in Surabaya

Integrating NLP Chatbot into Career Guidance Web Application

Artificial Intelligence (AI) is the development of computer systems that can perform tasks that typically require human intelligence. The development of an advanced web-based chatbot system involves the integration of various technologies and methodologies to create a seamless conversational interface. Leveraging HTML, CSS, and JavaScript for frontend development, alongside Python Flask framework for backend functionality, the project aims to deliver a robust and intuitive user experience. Key to the system's operation is the incorporation of PyTorch for machine learning, allowing for the implementation of sophisticated natural language processing (NLP) techniques. Through tokenization and stemming using NLTK (Natural Language Toolkit), the system enhances its understanding of user queries, enabling it to provide relevant and contextually appropriate responses. At the core of the system lies a supervised learning model trained on a custom dataset compiled from user interactions. This model utilizes bag-of-words representations and neural network architectures to predict responses based on input text. By continuously refining its predictions through feedback mechanisms, the system improves its accuracy and responsiveness over time, ensuring a more tailored and effective user experience. Voice input functionality, facilitated by speech recognition technology, further enhances user interaction by allowing users to communicate with the chatbot using spoken commands. This feature expands the accessibility and convenience of the system, catering to a broader range of users with varying preferences and abilities. In addition to its conversational capabilities, the system prioritizes security and privacy, implementing robust user authentication mechanisms and secure data storage practices. By adhering to industry best practices and standards, the project aims to instill trust and confidence in users regarding the confidentiality and integrity of their data.User interface design and usability are also key considerations in the development process, with careful attention given to creating an intuitive and visually appealing interface. Through thoughtful design choices and user feedback, the system seeks to optimize the user experience and streamline the interaction flow, ensuring that users can easily navigate and utilize its features. Overall, the project endeavors to create an intelligent and user-friendly chatbot system that enhances communication and interaction between users and technology. By leveraging advanced technologies and methodologies, the system aims to provide personalized assistance and support to users, ultimately improving productivity, efficiency, and satisfaction in various domains and applications

Video Summarization for Marathi Language

The Video Summarization Platform using Python Flask is a comprehensive tool designed to summarize Marathi and English videos while providing summaries in Hindi, Marathi, and English languages. Leveraging machine learning and natural language processing (NLP) techniques, this platform offers a sophisticated solution for efficiently extracting key information from videos. The platform begins by transcribing the audio content of the video into text using automatic speech recognition (ASR) technology. This transcription process ensures that the platform can accurately analyze and summarize the video's content. Next, the text is translated into the target languages, namely Hindi, Marathi, and English, enabling users from diverse linguistic backgrounds to access the summarized content. To generate concise and informative summaries, advanced NLP algorithm is applied. This algorithm analyze the transcribed text to identify the most significant phrases, sentences, and concepts. By considering factors such as keyword frequency, semantic relevance, and context, the platform effectively distils the video's content into digestible summaries. Additionally, machine learning models are employed to classify the type of video content. These models are trained on diverse datasets encompassing various video genres and topics. By recognizing patterns and features within the video content, the platform can accurately categorize videos into distinct types, such as news, interviews, tutorials, or entertainment. The platform's user interface, powered by Python Flask, offers a seamless experience for users to upload videos, select their preferred language for summarization, and receive concise summaries in their chosen languages. The intuitive design ensures accessibility and ease of use, catering to both novice and advanced users. Overall, the Video Summarization Platform serves as a valuable resource for individuals seeking efficient ways to consume multimedia content. Whether for educational, informational, or entertainment purposes, this platform empowers users to access summarized video content in multiple languages, facilitated by cutting-edge machine learning and NLP technologies. Key Words: Transcription, Marathi-speaking users, Marathi YouTube videos, video content, transcription, summary, translation, Natural Language Toolkit (NLTK), content comprehension, user interaction data, past summaries, recommendation

Human Language Data Processing using NLTK

Natural Language Toolkit (NLTK) is a comprehensive Python library designed to facilitate the exploration, analysis, and processing of human language data. With its extensive collection of tools, NLTK provides researchers, developers, and educators with a powerful platform for tasks ranging from basic text processing to advanced natural language understanding and machine learning. The toolkit includes modules for tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, syntactic parsing, semantic analysis, and more. Furthermore, NLTK offers access to numerous linguistic resources such as corpora, lexicons, and treebanks, making it an invaluable resource for both learning and research in the field of natural language processing (NLP). NLTK serves as an indispensable tool for unlocking the complexities of human language

The Spatial Distribution of Sexual Harassment Sentiment in Twitter

In a cultural landscape where discussions about sexual harassment are often considered taboo, this study leverages social media as a platform for the anonymous sharing of experiences related to sexual harassment in Asia. By focusing on Kuala Lumpur, we applied grid-based data mining to analyze the spatial distribution of tweets related to sexual harassment, revealing significant distribution patterns. Moreover, this study utilized two advanced sentiment analysis methods: the Natural Language Toolkit (NLTK) and Azure Machine Learning (AML), to evaluate the polarity of sentiments expressed in these tweets. The findings demonstrate a notable variance in sentiment analysis results between NLTK and AML, with NLTK classifying a majority of tweets as neutral (63.7%), while AML identified a predominant positive sentiment (70%). This discrepancy highlights the complexities of sentiment analysis and the importance of selecting appropriate tools for specific research contexts. In examining the spatial distribution, it becomes apparent that tweets collected through the identified keywords are dispersed within the boundaries of Kuala Lumpur. However, there is a notable concentration in specific areas, particularly evident in the hotspot encompassing Taman Desa and Pantai Dalam. Understanding the sentiment's location enables us to delve deeper into the pronounced cluster of tweets, examining potential correlations with surrounding social and moral issues. This insight empowers us to address the issue through various means, including the implementation of Crime Prevention through Environmental Design and fostering community-based initiatives.

The Comparative study of Python Libraries for Natural Language Processing (NLP)

Natural Language Processing (NLP) has seen significant advancements in recent years, driven largely by the availability of powerful Python libraries. This comparative study aims to analyze and compare the performance, language support, community support and ease of use of many popular Python libraries for NLP like NLTK (Natural Language Toolkit), spaCy, TextBlob, Flair, Jina, Gensim etc. The study evaluates these libraries across various NLP tasks such as tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and text summarization. Additionally, the paper discusses the strengths and weaknesses of each library, providing insights into their suitability for different NLP applications. Through detailed experimentation and analysis, this study aims to guide researchers and practitioners in selecting the most appropriate library for their NLP projects.

Enhancing Natural Language Processing (NLP) through VIKOR Method: A Comprehensive Approach for Improved Computational Linguistics

Natural Language Processing (NLP) is a revolutionary discipline that resides at the crossroads of computer science and linguistics, with its primary emphasis on the interaction between computers and human language. With roots in artificial intelligence, NLP seeks to equip machines with the ability to comprehend, interpret, and respond to natural language, enabling more intuitive and meaningful communication between humans and computers. This multidisciplinary domain leverages advanced algorithms and models to tackle a range of linguistic challenges, from language translation and sentiment analysis to speech recognition. Recent breakthroughs, particularly in deep learning and neural networks, have propelled NLP to new heights, with applications spanning diverse sectors such as healthcare, finance, and education. As NLP continues to advance, its potential impact on enhancing human-machine interaction and information processing is increasingly evident, promising a future where technology seamlessly integrates with our natural modes of communication. The significance of Natural Language Processing (NLP) in research lies in its capacity to revolutionize human-computer interaction. NLP empowers machines to understand and generate human language, enabling advancements in sentiment analysis, language translation, and conversational AI. This transformative capability has profound implications across various industries, including healthcare, finance, and education. NLP research not only enhances the efficiency of information retrieval and processing but also holds the key to developing more intuitive and user-friendly technologies. As the frontier of NLP research expands, its potential to bridge the communication gap between humans and machines continues to shape the future of technology and information access. VIKOR functions as a multi-criteria decision-making technique that determines the best option by comparing it to the ideal option. The ranking procedure comprises determining distances concerning the optimal solution. VIKOR uses linear normalization to get the best possible outcomes. This technique was first presented by Opricovic in 1998 and was intended to optimize multi-attribute complex systems. Its main focus is on ranking lists that allow for flexibility in the strategy weight interval and incorporate compromise alternatives to obtain desired results. Alternative taken as Open AI GPT-4, Google BERT, Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding, spaCy, NLTK (Natural Language Toolkit), Amazon Comprehend. Evaluation Parameter taken as Sentiment Analysis (F1 Score), NER Performance, Language Support, Processing Speed (Response Time) (ms), Translation Accuracy (BLEU Score), Data Privacy. The result it is seen that Amazon Comprehend is got the first rank where as is the IBM Watson Natural Language Understanding is having the lowest rank.

Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language

Natural language is a type of language that human beings use to communicate with each other. However, it is very difficult to communicate with a machine-understandable language. Finding context meaning is challenging the activity of automatically identifying machine translation, indexing engines, and predicting neighbor words in natural language. Many researchers around the world investigated word-sense disambiguation in different languages, including the Afaan Oromo language, to solve this challenge. Nevertheless, the amount of effort for Afaan Oromo is very little in terms of finding context meaning and predicting neighbor words to solve the word ambiguity problem. Since the Afaan Oromo language is one of the languages developed in Ethiopia, it needs the latest technology to enhance communication and overcome ambiguity challenges. So far, this work aims to design and develop a vector space model for the Afaan Oromo language that can provide the application of word-sense disambiguation to increase the performance of information retrieval. In this work, the study has used the Afaan Oromo word embedding method to disambiguate a contextual meaning of words by applying the semisupervised technique. To conduct the study, 456,300 Afaan Oromo words were taken from different sources and preprocessed for experimentation by the Natural Language Toolkit and Anaconda tool. The K-means machine learning algorithm was used to cluster similar word vocabulary. Experimental results show that using word embedding for the proposed language’s corpus improves the performance of the system by a total accuracy of 98.89% and outperforms the existing similar systems.