Exploring multimodal perspectives in collaborative writing: Sentiment analysis and word frequency in natural language processing

BackgroundQualitative research methods are increasingly being used across disciplines because of their ability to help investigators understand the perspectives of participants in their own words. However, qualitative analysis is a laborious and resource-intensive process. To achieve depth, researchers are limited to smaller sample sizes when analyzing text data. One potential method to address this concern is natural language processing (NLP). Qualitative text analysis involves researchers reading data, assigning code labels, and iteratively developing findings; NLP has the potential to automate part of this process. Unfortunately, little methodological research has been done to compare automatic coding using NLP techniques and qualitative coding, which is critical to establish the viability of NLP as a useful, rigorous analysis procedure.ObjectiveThe purpose of this study was to compare the utility of a traditional qualitative text analysis, an NLP analysis, and an augmented approach that combines qualitative and NLP methods.MethodsWe conducted a 2-arm cross-over experiment to compare qualitative and NLP approaches to analyze data generated through 2 text (short message service) message survey questions, one about prescription drugs and the other about police interactions, sent to youth aged 14-24 years. We randomly assigned a question to each of the 2 experienced qualitative analysis teams for independent coding and analysis before receiving NLP results. A third team separately conducted NLP analysis of the same 2 questions. We examined the results of our analyses to compare (1) the similarity of findings derived, (2) the quality of inferences generated, and (3) the time spent in analysis.ResultsThe qualitative-only analysis for the drug question (n=58) yielded 4 major findings, whereas the NLP analysis yielded 3 findings that missed contextual elements. The qualitative and NLP-augmented analysis was the most comprehensive. For the police question (n=68), the qualitative-only analysis yielded 4 primary findings and the NLP-only analysis yielded 4 slightly different findings. Again, the augmented qualitative and NLP analysis was the most comprehensive and produced the highest quality inferences, increasing our depth of understanding (ie, details and frequencies). In terms of time, the NLP-only approach was quicker than the qualitative-only approach for the drug (120 vs 270 minutes) and police (40 vs 270 minutes) questions. An approach beginning with qualitative analysis followed by qualitative- or NLP-augmented analysis took longer time than that beginning with NLP for both drug (450 vs 240 minutes) and police (390 vs 220 minutes) questions.ConclusionsNLP provides both a foundation to code qualitatively more quickly and a method to validate qualitative findings. NLP methods were able to identify major themes found with traditional qualitative analysis but were not useful in identifying nuances. Traditional qualitative text analysis added important details and context.

This chapter discusses the concept of natural language processing (NLP), specifically how the NLP is applied. The chapter further outlines the typical functions of the NLP. In addition, it explores the NLP in strategy and strategy implementation.The NLP is a branch of computer science, AI and linguistics that focuses on the interaction between computer programs and human language. There are three functions of the NLP, namely, machine translation (MT), text summarisation and sentiment analysis (SA). MT is a branch of computer linguistics that analyses the use of computerised tools to translate the context derived from this, from one language to the next. The language that is being translated is the human language.Automatic text summarisation refers to a process of bringing about a succinct and meaningful summary of the text. SA is a way of finding out the polarity or strength of the opinion that is expressed in written text. The opinion could be either positive or negative. MT has three major approaches, namely, rule-based MT, statistical MT and neural MT. A rule-based system would typically require the expert to know both the source and the target languages. This knowledge is then used to develop syntactic, semantic and morphological rules to achieve the translation. The statistical approach uses statistical models based on the analysis of bilingual text corpora. The neural MT approach uses neural networks to achieve machine translation. There are two broad categories of text summarisation approaches, namely, extractive summarisation and abstractive summarisation. Furthermore, there are four popular types of SA, namely, the fine-grained SA, emotion detection, aspect-based SA and multilingual SA. The firm that deploys the NLP and ML in Big Data Analytics would gain crucial and better insights, setting the firm apart from its competitors.

Opinion Mining and Sentiment Analysis in Natural Language Processing (NLP) are challenging, as they require deep understanding. Understanding involves methods that could differentiate between the facts of explicit and implicit, regular and irregular, syntactical and semantic language rules. Researches oriented towards Natural Language Processing and Sentiment Analysis have many unresolved problems like co-reference resolution, negation handling, anaphora resolution, named-entity recognition, and word-sense disambiguation. This paper is proposed to develop an Optimized Partial Ancestral Graph (O-PAG) which is capable of mining patterns in human interactions and compare it with an existing tree based pattern mining approach. The experimental results are exposed to number of frequent interactions made and execution time. Results indicate that the overall performance can reach considerable improvements on using O-PAG approach.

Discourse analysis plays a significant role in understanding and interpreting the complexity of human language, providing valuable insights for improving natural language processing (NLP) systems. This study presents an in-depth review of discourse analysis in NLP, and its applications in various domains such as machine translation, question-answering systems, sentiment analysis, conversational bots, and information retrieval. By analyzing the structural and contextual aspects of language, discourse analysis enhances the NLP system’s ability to understand individual words and their meaning in cross-border conversational contexts. The review highlights how discourse analysis and learning techniques work together to improve the automated system's accuracy and efficiency. In addition to addressing significant challenges, including ambiguity, cultural variance, and conversational dynamics, the paper also suggests future research directions for exploring the possibilities of natural language processing in human-computer interactions. This work attempts to fill the gap between linguistic theory and computational applications by demonstrating how discourse analysis can revolutionize the development of more intelligent, contextually aware, and human-centered language technologies.

Natural language processing (NLP) is a subset of artificial intelligence demonstrating how algorithms can interact with individuals in their unique languages. In addition, sentiment analysis in NLP is better in numerous programs, including evaluating sentiment in Telugu. Several unsupervised machine-learning algorithms, such as k-means clustering with cuckoo search, are used to detect Telugu text. However, these techniques struggle to cluster data with variable cluster sizes and densities, slow search speeds, and poor convergence accuracy. This study developed a unique ML-based sentiment analysis system for Telugu text to address the shortcomings. Initially, in the pre-processing stage, the proposed Linear Pursuit Algorithm (LPA) removes words in white spaces, punctuation, and stops. Then, for POS tagging, this research proposed a Conditional Random Field with Lexicon weighting; following that, a Contrived Passive Aggressive with Fuzzy Weighting Classifier (CPSC-FWC) is proposed to classify the sentiments in Telugu text. Consequently, the method we propose produces efficient outcomes in terms of accuracy, precision, recall, and f1-score.

Now-a-days, Social media platforms like Facebook, Twitter etc. are becoming more and more popular to express feelings and thoughts. People not only share their happy moments on these platforms but also share their feelings when they are extremely depressed. Analyzing these social media posts, one’s mental condition can be detected whether he/she is happy, sad or angry at a particular time using sentiment analysis in natural language processing. Most of the research in this field is based on English language and the accuracy of sentiment analysis from Bengali language is not very high. So, our purpose is to work on this field using Bengali dataset collected from different social media posts and make the sentiment detection more accurate so that this work can be used to build a system that can be used in the mental health sector of our country. In this research, we have first collected social media data. After applying different data preprocessing techniques, we have made a number of feature selection and extraction combinations. We have applied some basic and advanced machine learning and deep learning algorithms on each of these to find the best combination and algorithm by monitoring the highest achieved accuracy.

Human communication is predominantly expressed through speech and writing, which are powerful mediums for conveying thoughts and opinions. Researchers have been studying the analysis of human sentiments for a long time, including the emerging area of bimodal sentiment analysis in natural language processing (NLP). Bimodal sentiment analysis has gained attention in various areas such as social opinion mining, healthcare, banking, and more. However, there is a limited amount of research on bimodal conversational sentiment analysis, which is challenging due to the complex nature of how humans express sentiment cues across different modalities. To address this gap in research, a comparison of multiple data modality models has been conducted on the widely used MELD dataset, which serves as a benchmark for sentiment analysis in the research community. The results show the effectiveness of combining acoustic and linguistic representations using a proposed neural-network-based ensemble learning technique over six transformer and deep-learning-based models, achieving state-of-the-art accuracy.

Purpose This study examines the evolution of education research in Kazakhstan from 1992 to 2024, a period marked by profound transformation, aiming to assess how scholarly output aligns with national education policy and broader developmental priorities. Design/methodology/approach A mixed bibliometric and natural language processing (NLP) approach was employed to analyze 956 English-language publications indexed in the Web of Science. The study combined quantitative analyses of publication trends, authorship and institutional collaboration with NLP techniques, including keyword co-occurrence, topic modeling and sentiment analysis, framed by a review of Kazakhstan’s education policy phases: post-Soviet foundational reforms and an era of global alignment. Findings Results reveal a substantial surge in research output, particularly after 2020, driven by a core set of highly productive authors and institutions. Thematic analyses indicate partial alignment with policy priorities, with multilingual education, digitalization and pedagogical innovation prominent, while inclusive education and social justice appear less substantively addressed. Collaboration networks display a core-periphery structure, with Nazarbayev University acting as the primary international and national hub. Originality/value This study offers the first comprehensive, longitudinal mapping of Kazakhstani education research, integrating bibliometric and NLP methods with policy analysis. It highlights both the rapid quantitative growth and the structural and thematic limitations of the field, providing a foundation for policy and institutional strategies aimed at fostering more diverse, critical and sustainable research.

IntroductionThe emergence of social media platforms like X has created a unique space for mental health discussions. This study aims to analyze the language and themes used in social media discussions to appreciate sentiments about PMS and PMDD by looking at a sample of the most popular tweets on platform X.ObjectivesWe hypothesize that this content can provide insight into public perceptions and guide educational campaigns.MethodsAn advanced Twitter/X search for “PMS” and “PMDD” was conducted, filtering for English content. The top 100 tweets for each search term were explored through two different sentiment analysis tools which include Dr. Daniel Soper’s Sentiment Analyzer Tool and Text2Data Application Programming Interface (API) Natural Language Processing (NLP) Analysis. Tweets were also analyzed using a word cloud generator to identify the most frequently used terms. Connecting words were eliminated from the final output.ResultsNegative sentiment was more prevalent than positive among the tweets for PMDD, with an overall sentiment analysis of an average of -24.3 per the Daniel Soper Sentiment Analyzer tool, suggesting a negative and serious tone. The most frequently appearing terms in these tweets were “month” (mentioned 17 times), “bad” (16), “love” (15), “feel” (14), “MAFS” (14), “support (14). Per Text2Data’s API NLP analysis, the top 150 words had a negative sentiment of -0.59 with a magnitude of 1.69. Negative sentiment was more prevalent than positive among the tweets for PMS, with an overall sentiment analysis of an average of -15.2 per the Daniel Soper Sentiment Analyzer tool, suggesting a somewhat negative and serious tone. The most frequently appearing terms in these tweets were “new” (11), “price” (10), “oxford” (9), “feel” (14), “people” (9), “want” (9). Per Text2Data’s API NLP analysis, the top 150 words had neutral sentiment of +0.15 with a magnitude of 1.59.ConclusionsThis study emphasizes a less negative sentiment associated with PMS compared to PMDD. It also highlights how more emotionally charged terms were used among tweets discussing PMDD compared to PMS. This may reflect public perception of the two conditions. Additionally, social media can be a way to gauge public interest and perception of medical topics.Disclosure of InterestNone Declared

Research on lifestyle changes during the COVID-19 pandemic often relies on Likert-type scale question surveys. Survey participants respond to questions by selecting one of the numerically ordered choices, "Strongly Disagree"=1, "Disagree"=2, "Neutral"=3, "Agree"=4, and "Strongly Agree"=5. Analyzing Likert-type data requires statistical methods beyond approaches like linear regression. First, it is unclear if the distance between choices are truly equal. For example, are "Agree" and "Strongly Agree" more close than "Neutral" and "Agree? Second, summarizing results using traditional means makes little sense. For example, would a mean of 4.5 imply "Agree and a half"? Finally, participants tend to select more central choices and less extremes.

In recent years, Unicode has been standardized; and with the penetration of SNSs, the use of emojis has become common. Emojis, as they are also known, are most efective in expressing emotions in sentences. Sentiment analysis in natural language processing so far has involved learning by manual labeling of sentences. By using suitable emojis estimated from sentences, people might express their emotions more clearly and laconically. In this paper, we propose a new model that learns from sentences using emojis as labels, collecting Japanese tweets from Twitter as the corpus. We verify and compare multiple models based on EncoderDecoder Model of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Our sophisticated experiments demonstrate that emojis are efective in expressing tweet emotion.

Natural language is a crucial tool to facilitate communication in our day-to-day activities. This can be achieved either in text or speech forms. Natural language processing (NLP) involves making computers understand and process natural language. NLP has enhanced the way humans interact with computers, from having computers use speech to talk to humans as well as having computers translate human speech. Apart from speech, computers also create and understand sentences in natural language in a process called morphological analysis. Morphological analysis is an important part in natural language processing, being applied as a preprocessing step in most NLP tasks. Morphological analysis consists of four subtasks, that is, lemmatization, part-of-speech (POS) tagging, word segmentation and stemming. In this paper, we explore in detail each of these tasks of morphological analysis. We then evaluate the techniques used in this NLP field. Finally, we give a summary of the results of each of these techniques.

Natural language is a crucial tool to facilitate communication in our day-to-day activities. This can be achieved either in text or speech forms. Natural language processing (NLP) involves making computers understand and process natural language. NLP has enhanced the way humans interact with computers, from having computers use speech to talk to humans as well as having computers translate human speech. Apart from speech, computers also create and understand sentences in natural language in a process called morphological analysis. Morphological analysis is an important part in natural language processing, being applied as a preprocessing step in most NLP tasks. Morphological analysis consists of four subtasks, that is, lemmatization, part-of-speech (POS) tagging, word segmentation and stemming. In this paper, we explore in detail each of these tasks of morphological analysis. We then evaluate the techniques used in this NLP field. Finally, we give a summary of the results of each of these techniques.

Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing (NLP) and text analysis to systematically identify, extract and quantify states and subjective information. Transfer learning (TL) [1] is a study in machine learning focusing on storing knowledge gained while solving one problem and applying it to a different but related problem. In NLP, recent results also demonstrated the effectiveness of models using pre-training on a language modeling task [2], [3]. The transfer learning based models help to rapidly increase understanding of words and sentences arrangement in which semantics and connections are easily grasped. In this paper, we present the results from applying BERT [16], a transfer learning method, in Vietnamese benchmark [17] for one of text classification problems, the Aspect Based Sentiment Analysis problem. The experiments were conducted on two data sets, named Hotel and Restaurant [17], in two task (A) Aspect Detection and (B) Aspect Polarity. The obtained results have outperformed some previous systems [18]-[20] in precision, recall, as well as the F1 measures.

Automated sentimental analysis using heuristic-based CNN-BiLSTM for E-commerce dataset