#MultipleSclerosis: Artificial intelligence-based sentiment and content analysis of Tweets.

BackgroundThe dissemination of rumor rebuttal content on social media is vital for rumor control and disease containment during public health crises. Previous research on the effectiveness of rumor rebuttal, to a certain extent, ignored or simplified the structure of dissemination networks and users’ cognition as well as decision-making and interaction behaviors.ObjectiveThis study aimed to roughly evaluate the effectiveness of rumor rebuttal; dig deeply into the attitude-based echo chamber effect on users’ responses to rumor rebuttal under multiple topics on Weibo, a Chinese social media platform, in the early stage of the COVID-19 epidemic; and evaluate the echo chamber’s impact on the information characteristics of user interaction content.MethodsWe used Sina Weibo’s application programming interface to crawl rumor rebuttal content related to COVID-19 from 10 AM on January 23, 2020, to midnight on April 8, 2020. Using content analysis, sentiment analysis, social network analysis, and statistical analysis, we first analyzed whether and to what extent there was an echo chamber effect on the shaping of individuals’ attitudes when retweeting or commenting on others’ tweets. Then, we tested the heterogeneity of attitude distribution within communities and the homophily of interactions between communities. Based on the results at user and community levels, we made comprehensive judgments. Finally, we examined users’ interaction content from three dimensions—sentiment expression, information seeking and sharing, and civility—to test the impact of the echo chamber effect.ResultsOur results indicated that the retweeting mechanism played an essential role in promoting polarization, and the commenting mechanism played a role in consensus building. Our results showed that there might not be a significant echo chamber effect on community interactions and verified that, compared to like-minded interactions, cross-cutting interactions contained significantly more negative sentiment, information seeking and sharing, and incivility. We found that online users’ information-seeking behavior was accompanied by incivility, and information-sharing behavior was accompanied by more negative sentiment, which was often accompanied by incivility.ConclusionsOur findings revealed the existence and degree of an echo chamber effect from multiple dimensions, such as topic, interaction mechanism, and interaction level, and its impact on interaction content. Based on these findings, we provide several suggestions for preventing or alleviating group polarization to achieve better rumor rebuttal.

Determining patients' perceptions of multiple sclerosis, a disease with varying symptoms and prognosis for each individual, can significantly contribute to directing care and treatment. Metaphors may be an opportunity to determine perceptions of this unique illness experience. The aim of this study was to reveal the perceptions of patients with multiple sclerosis about "multiple sclerosis" through metaphors. This study was conducted with metaphor analysis technique based on phenomenological method. The sample included 184 patients with multiple sclerosis. Data was collected face-to-face between July 2022 and January 2023. Each participant was interviewed individually and was asked to fill in the blanks in the sentence, "multiple sclerosis is like… because…". Content analysis was performed for metaphors. The study revealed five main themes and twelve subthemes that provided insight into the participants' multiple sclerosis perceptions. The themes address (i) Manipulator multiple sclerosis; ambiguous multiple sclerosis, attritive multiple sclerosis, controller multiple sclerosis, demander multiple sclerosis, and conditional multiple sclerosis (ii) Temporal multiple sclerosis; cyclical multiple sclerosis and perpetual multiple sclerosis (iii) Follower multiple sclerosis; unaccepted multiple sclerosis, partner multiple sclerosis and ambusher multiple sclerosis (iv) Different multiple sclerosis; bittersweet multiple sclerosis and unique multiple sclerosis (v) Restorative multiple sclerosis. This study demonstrated that patients with multiple sclerosis mostly had negative perceptions regarding their relationship with multiple sclerosis. The results place a responsibility on healthcare professionals to improve how patients adapt to multiple sclerosis. This study's results can bridge theoretical knowledge and practice.

Twitter is attracting significant interests from the research community in the last few years. Sentiment analysis of tweets is among the hottest topics of research nowadays. State of the art approaches of sentiment analysis present many shortcomings when classifying tweets, in particular when the classification goes beyond the binary or ternary classification. Multi-class sentiment analysis has proven to be a very challenging task. This is mainly for the simple reason that a tweet usually does not contain a single sentiment, but many ones. In this paper, we propose a pattern-based approach for sentiment quantification in Twitter. By quantification, we refer to the detection of the existing sentiments within a tweet and the detection of the weight of these sentiments. In a first step, we classify tweets into positive, negative, or neutral. Our approach reaches an accuracy of 81%. We then perform the sentiment quantification on the sentimental tweets (i.e., positive and negative ones) to extract the sentiments within them: we define 5 positive sentiment sub-classes 5 negative ones and detect which exist in each tweet. We define 2 metrics to measure the correctness of sentiment detection, and prove that sentiment quantification can be a more meaningful task than the regular multi-class classification.

The study analyses Turkish and English tweets about climate change on the social media platform Twitter and comparatively examines individuals” perceptions, concerns, and emotional reactions to this issue. A total of 2,046 Turkish and 18,000 English tweets were collected; 1,104 Turkish and 6,449 English tweets were analyzed after the cleaning process. Artificial intelligence-based methods such as text mining, sentiment analysis, and topic modelling are used. Topic modelling with Latent Dirichlet Allocation (LDA) identified prominent themes in tweets in both languages. Sentiment analysis is performed using deep learning techniques to categories tweets into positive, negative, and neutral categories. The findings show that English tweets contain stronger emotional reactions, while Turkish tweets contain a higher proportion of neutral expressions. Additionally, it was observed that the perception of climate change can differ in local and global contexts. Based on a multidimensional analysis of social media data, the study provides valuable insights into the development of environmental communication strategies. The comparison of Turkish and English tweets contributes to understanding the effects of cultural contexts on climate change perception. The findings have important implications for policymakers and environmental awareness campaigns, as they highlight the need for tailored communication strategies that consider cultural differences in climate change perception.

This paper reports on using RStudio packages to conduct a text and sentiment analysis of tweets from SABCNews. To download selected tweets, the twitteR package in R was used. A 10-day period was to collect data (tweets). In this regard, a set of 1000 tweets written in English were analysed from the @SABCNews. This analysis was performed to classify each tweet as positive, negative or neutral and to what degree; thus, learn more about the audience, transform every interaction into an actionable strategy for success, and shape the company's business strategy. The tweeting pattern is measured using Hashtag and Favorite Count, Source of Tweet, and Count of Tweet compared to Favorite Count and Retweet Count. Based on trust, joy, surprise, anticipation, fear, sadness, disgust, and anger, it can be observed that most tweets expressed trust and, joy; with no tweet expressing anger. Furthermore, the Twitter sentiment analysis assigned for each tweet showed that a rapid change in sentiments for each tweet occurs late at night before individuals sleep and early morning before they go to their daily duties. While these results are positive for SABCNews; however, they emanate from only English language tweets. Therefore, future work will focus on sentiments expressed in other South African languages and compare views from other news agencies.

As of early 2019, the COVID-19 outbreak has ensued in millions of deaths, making it one of the worst pandemics in history. In addition to wearing masks, increasing sanitation, and avoiding crowds, widespread vaccination is crucial for preventing virus transmission. Despite significant progress in vaccine research and policy implementations, widespread immunization remains challenging. Analysis of exchanges on social media regarding COVID-19 vaccines has revealed significant uncertainty and mistrust in vaccines. As a result, ongoing evaluation of trust and confidence in COVID-19 vaccines is critical to crafting successful communication approaches for promoting extensive vaccination. This study aims to use content analysis of tweets about COVID-19 vaccines while also examining the user accounts generating them to provide evidence of fluctuations in public views toward COVID-19 vaccines. The proposed framework collects and processes data from social media networks, particularly Twitter, before presenting various analytics based on the different analyses performed through machine learning and deep learning algorithms. We hypothesize that a qualitative study starting from the pandemic would identify themes in public discourses (particularly those with negative sentiment or evidence of misleading information) that circulated during the developmental and mass release phases of COVID-19 vaccines. Therefore, it could inform and aid healthcare officials, public health agencies, and policymakers in increasing awareness and educational interventions for COVID-19 vaccines.

The interaction between human demand and urban greenspace supply for promoting positive emotions with sentiment analysis from twitter

The article formulates a rigorously developed concept of opinion mining and sentiment analysis using hybrid neural networks. This conceptual method for processing natural-language text enables a variety of analyses of the subjective content of texts. It is a methodology based on hybrid neural networks for detecting subjective content and potential opinions, as well as a method which allows us to classify different opinion type and sentiment score classes. Moreover, a general processing scheme, using neural networks, for sentiment and opinion analysis has been presented. Furthermore, a methodology which allows us to determine sentiment regression has been devised. The paper proposes a method for classification of the text being examined based on the amount of positive, neutral or negative opinion it contains. The research presented here offers the possibility of motivating and inspiring further development of the methods that have been elaborated in this paper.KeywordsComputational linguisticsNatural language processingNeural networksOpinion miningSentiment analysis

Overview: The rise of e-commercehas created a more interactive and competitive environment for both sellers and buyers. In recent times, a heavy reliance on online product reviews posted on the e-commerce website by existing and verified users has been witnessed. From the extant literature, it is evident that the impact of non–textual or quantitative aspects of online product reviews (OPR), such as volume, valence, star rating, helpful votes, etc., on buying behaviour, purchase intention, sales, etc. has been vastly studied. But since the past decade, the importance of analysing and understanding the textual data of OPR has been strongly recommended and explored.Aim: Reviewers express their opinions through non-structured and unfiltered reviewswhere the information presented goes beyond mere words.The review text has embedded emotions and sentiments also. The current research aims to investigate and comprehend the implicit information conveyed by consumers in the unstructured and heartfelt reviews on Amazon.in. The study seeks to identify key semantic aspects affecting OPRs' assessment by highlighting latent topics, sentiments and intricacies within the textual content of the reviews. Methodology: Amazon is the most popular e-commerce site in India, and its review system is widely seen as reliable, clear, and standardised. Its multi-dimensional format—textual reviews(review statements and title statements), aggregate star ratings which guarantee consistency across many product categories and offers a solid foundation forqualitative research. This study employed a multistage sampling technique to extract 5,900 reviews from 59 top-selling products across three best-selling categories: beauty, fashion, and electronics from Amazon India. Web scraping has been used to extract the review data using Python as a programming language. A qualitative content analysis, topic modelling, sentiment analysis and sentiment score analysis have been employed using various packages and functions in R Studio.Findings: The research reveals that both negative and positive sentiments have significant effects on product ratings. The word count analysis indicated a predominant use of positive words such as ‘good’, ‘product’, ‘quality’, ‘nice’, and ' price’. The three distinct topics identified through topic modelling demonstrate that reviews are shaped by a combination of functional utility, sensory experience, and detailed product attributes. The sentiment analysis revealed that positive sentiment is more common than negative sentiment. Additionally, emotions like trust, anticipation and joy were predominant, while negative emotions such as disgust and anger were less frequently observed. A key finding is the asymmetrical effect, where negative sentiment has a notably stronger negative impact than positive sentiment.Although statistically significant, the relatively low R-squared value suggests that sentiment scores alone account for only a small part of the variance in product ratings, indicating that ratings are a complex outcome influenced by multiple factors beyond expressed sentiment. Implications: The study highlights the multifaceted role of online product reviews in shaping consumer behaviour on e-commerce platforms. The predominance of positive emotions emphasises the value of building strong consumer confidence. At the same time, the asymmetrical effect of negative sentiment urges the need for businesses to address the dissatisfaction promptly. For practitioners, the findingssuggest that effective product management involves not only mitigating negative reviews but also actively leveraging positive consumer emotions and experiences to strengthen brand loyalty. Strategically, e-commerce businesses should adopt a dual approach—proactively managing negative feedback while amplifying positive narratives—to enhance customer satisfaction and trust. Academically, the research contributes by evidencing the interplay of emotional, semantic, and many other factors in review analysis, offering pathways for future studies to incorporate richer variables and advanced text mining techniques.

Organisations these days are actively using social media platforms to engage with potential and existing customers and monitor what they say about the organisation’s product or service. The most important area within social media monitoring lies in how to gain insight for sentiment analysis. Sentiment analysis helps in effective evaluation of customer’s sentiments in real time and takes on a special meaning in the context of online social networks like Twitter and Facebook, which collectively represent the largest online forum available for public opinion. Sentiment Analysis is not about retrieving and analyzing the analytics purely on the basis of positive, negative or neutral sentiment. It is imperative to assess the influencers of the sentiments in terms of Retweet and Share option used by them on Twitter and Facebook platform respectively. Measuring the intensity is other important aspect of sentiment analysis process. What kind of nouns, adjectives, verbs and adverbs are used in the opinion across the Twitter and Facebook platform matters as well since it exhibits the intensity of the underlying emotion in the text written. This study was conducted to propose a framework to identify and analyse the positive and negative sentiments present in Twitter and Facebook platforms and an algorithm was prepared to measure the intensity and influence of the positive, negative sentiment in particular using the document and sentence level analysis technique.

Extracting sentiment from Twitter data is one of the fundamental problems in social media analytics. Twitter's length constraint renders determining the positive/negative sentiment of a tweet difficult, even for a human judge. In this work we present a general framework for per-tweet (in contrast with batches of tweets) sentiment analysis which consists of: (1) extracting tweets about a desired target subject, (2) separating tweets with sentiment, and (3) setting apart positive from negative tweets. For each step, we study the performance of a number of classical and new machine learning algorithms. We also show that the intrinsic sparsity of tweets allows performing classification in a low dimensional space, via random projections, without losing accuracy. In addition, we present weighted variants of all employed algorithms, exploiting the available labeling uncertainty, which further improve classification accuracy. Finally, we show that spatially aggregating our per-tweet classification results produces a very satisfactory outcome, making our approach a good candidate for batch tweet sentiment analysis.

We introduce Big Data Analytics (BDA) and Sentiment Analysis (SA) to the study of international negotiations, through an application to the case of the UK-EU Brexit negotiations and the use of Twitter user sentiment. We show that SA of tweets has potential as a real-time barometer of public sentiment towards negotiating outcomes to inform government decision-making. Despite the increasing need for information on collective preferences regarding possible negotiating outcomes, negotiators have been slow to capitalise on BDA. Through SA on a corpus of 13,018,367 tweets on defined Brexit hashtags, we illustrate how SA can provide a platform for decision-makers engaged in international negotiations to grasp collective preferences. We show that BDA and SA can enhance decision-making and strategy in public policy and negotiation contexts of the magnitude of Brexit. Our findings indicate that the preferred or least preferred Brexit outcomes could have been inferred by the emotions expressed by Twitter users. We argue that BDA can be a mechanism to map the different options available to decision-makers and bring insights to and inform their decision-making. Our work, thereby, proposes SA as part of the international negotiation toolbox to remedy for the existing informational gap between decision makers and citizens’ preferred outcomes.

Due to its ease and popularity, social media has recently become an essential source of data for researchers and various stakeholder groups that seek a reliable assessment of their policies based on a comprehensive understanding of users' feedback and inputs, which are reflected in their posts and discussions. In this study, we investigate the issue of users' car parking behaviour through a comprehensive analysis of related hashtags collected from the Twitter social media platform. For this purpose, we adopted a two‐step strategy where in the first stage, a surface‐level analysis of the identified hashtags involving inductive reasoning, sentiment analysis, and user interaction in terms of engagement and diversity scores is performed. In the second phase, a tweet content analysis is performed using sentiment analysis and Empath categorization with respect to the most frequent wordings (assimilating to separate topics), in the same spirit as aspect‐sentiment analysis, to gain further insights regarding the occurrence of negative and positive posts. A quantitative evaluation of the coherence of the Empath categorization indicates that achieved 0.83% coherence score and outperformed both LDA and LSA that had a score of 0.77% and 0.76% respectively. Furthermore, the common word technique assimilated to aspect sentiment compared to the state‐of‐the‐art model for aspect sentiment‐based deberta‐v3‐base model. Besides, the influence of bots or spammers is evaluated using engagement/diversity measures and Botometer API. The results provide valuable insights in terms of discriminating between positive and negative posts and the correlation of surface‐level analysis with content‐based analysis, as well as the impact of various categorizations. The results expect to enable urban planners and policymakers to advance evidence‐based policing in the future design of intelligent parking systems.

Blogs, internet forums, social networks, and micro-blogging sites are some of the growing number of places where users can voice their opinions. Opinions on any given product, issue, service, or idea are contained in data, making them a valuable resource in their own right. Popular social networking services like Twitter, Facebook, and Google+ allows expressing views on a variety of topics, participating in discussions, or sending messages to a global user. Twitter sentiment analysis has received a lot of attention recently.Sentiment analysis is finding how a person feels about a topic from their written response about it and it can be separated into positive and negative through its use. Doing so enables to classify the tweets made by a user in to appropriate classification category based on which some decisions can be made. The literature proposed approaches to develop the classifiers on the Twitter datasets. Operations, including tokenization, stop-word removal, and stemming will be performed. NLP converts the text to a machine-readable representation. Artificial Intelligence (AI) combines NLP data to evaluate if a situation is positive or negative. The document’s subjectivity can be identified using ML and NLP techniques to categorize them in to positive, neutral, or negative. Performing sentiment analysis in Twitter data can be tedious due to limited size, unstructured nature, misspellings, slang, and abbreviations. For this task, a Tweet Analyzing Model for Cluster Set Optimization with Unique Identifier Tagging (TAM-CSO-UIT) was built using prospects to determine positive or negative sentiment in tweets obtained from Twitter. This approach assigns a +ve/-ve value to each entry in the Tweet database based on probability assignment using n-gram model. To perform this effectively the tweet dataset is considered as a sliding window of length L. The proposed model accurately analyses and classifies the tweets.

Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity. Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs. In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English tweets. Word embedding is a notable arrangement of procedures that can remember words linguistics dependent on the spread theory which expresses that word is utilized and happens within the same settings tend to indicate comparable implications. Bag of words is an approach used in natural language processing to retrieve information and features from written documents. For the bag of words, machine learning techniques like naive bayes, decision tree, k-nearest neighbor, and support vector machine is used to enhance the accuracy. For word embedding the neural network technique is proposed by the combination of recurrent neural network (RNN) with long-short term memory (LSTM) for sentimental analysis of tweets. Datasets of Urdu and English tweets are used for negative and positive classification tweets with machine learning techniques. The contribution of this paper involves the implementation of a hybrid approach that focused on a sentiment analyzer to overcome social network challenges and also provided the comparative analysis of different machine learning algorithms. The results indicate improvement while using the combination of RNN with the help of LSTM showed accuracy 87% on the Urdu dataset and 92% on the English dataset.