Parametrically modified BERT for emotion prediction through sentiment analysis

Navigating Social Media in #Ophthalmology

In the digital world, social media has become one of the most popular communication mediums that allow users to share their views on various topics in their social network. For example, Twitter users are allowed to share their thoughts on various topic by sending tweets with a maximum length of 140 characters. Hence, social media driven information contains opinions and sentiments on various topics of interest which are extremely useful for companies to design marketing strategies. Sentiment Analysis is widely used to assist people to understand the massive amount of data available online and identify the polarity of the topical based social media opinions. However, social media platforms’ users come from all over the world and have variation in terms of informal language and short notation used on social media platforms. Therefore, the identification on the polarity of topical social media has become more challenging and the accuracy on the polarity of topical social media opinions might be influenced. This paper investigates the effectiveness of applying different spelling correction algorithms, such as Levenshtein distance and Peter Norvig’s algorithm for spelling correction of unknown words found in social media such as Twitter, before carrying out sentiment analysis. The evaluation of spelling correction algorithms on sentiment analysis is carried out by comparing the polarities of manually annotated tweets with the polarities obtained from the sentiment analysis algorithm. Based on the results obtained, there are slight improvements in term of percentage of matched polarity, where 1.6% improvement by using the Levenshtein distance-based algorithm and 2.0% improvement by using the Peter Norvig’s algorithm.

This chapter delves into the intersection of social media and public health, specifically focusing on four key methodologies: sentiment analysis, social network analysis, topic modeling, and trend analysis. The scope of the review encompasses these distinct but interconnected methodologies and their applications in the context of public health and the healthcare industry. Our findings indicate that these techniques offer significant potential for extracting valuable insights from social media data, which can inform public health initiatives and policies. Sentiment analysis provides a pulse on public opinion on health-related topics; social network analysis reveals patterns of communication and information dissemination; topic modeling identifies trending health topics on social media platforms; and trend analysis helps in understanding the evolution of these trends over time. The review concludes that the integration of these methodologies with social media data presents a rich avenue for both research and practical applications in public health. As social media continues to evolve, so will the opportunities for public health research and practice, underscoring the need for continued exploration and refinement of these techniques.

Sentiment analysis of social media comments plays a crucial role in understanding public opinion and sentiment trends. This study focuses on sentiment analysis of Weibo comments, a popular microblogging platform in China, using a (CNN) approach. We propose a novel methodology that leverages the inherent structural dependencies among comments and users in the Weibo network to capture nuanced sentiment patterns. By representing Weibo comments as a graph, with users and comments as nodes and their interactions as edges, we exploit the relational information to enhance sentiment classification accuracy. Furthermore, we employ attention mechanisms to prioritize influential users and comments in the sentiment analysis process. Through extensive experiments on real Weibo datasets, our proposed CNN-based sentiment analysis framework demonstrates superior performance compared to traditional methods, achieving high accuracy in sentiment classification tasks. This research contributes to advancing sentiment analysis techniques in social media platforms and provides valuable insights for understanding public sentiment dynamics in online communities like Weibo.

The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progresswith contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis, the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.

An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

This thesis examines the issue of ill-formed text (microtext) and shows its impact on sentiment analysis. The source of microtext is Twitter, WhatsApp, Facebook and other social media platforms. Words or phrases not in their standard language format like “lol” (laugh out loud), “c u 2nite” (see you tonight) and “plz” (please) are called out of vocabulary (OOV) terms. On the other hand, words or phrases in their standard forms, for example, “tonight”, “love”, or “talk to you later”, are called In Vocabulary (IV) terms. The motivation behind microtext normalization is its growing presence on both online and offline platforms. Microtext might appear to be an ill-formed text, which requires find-and-replace methods. Traditionally, in a Natural Language Processing (NLP) context, microtexts such as abbreviations and length shortening or extending are normalized, but phonemic and graphemic variation is often ignored. However, the features of microtext can be traced back to social, cognitive and geographic influences. To understand social media text, it is essential to develop a microtext normalization module for traditional and modern NLP models. The first section of this thesis focuses on combining unsupervised learning methods in 2 different chapters, 4 and 5. The first method includes lexicon creation to transform the most frequent microtexts and emoticons on social media and applies them to sentiment analysis. This method shows an accuracy increase of 4% when microtext normalization is applied before sentiment analysis. This method is extended with an application to a pre-trained chatbot to improve the chatbot’s understanding of microtext (social media slangs). This method shows a mean BLEU score of 0.8 for the SMS and Tweet dataset. The second method incorporates a phonetic based approach to transform any microtext into its phonetic equivalent before 1normalizing it. This method shows an accuracy improvement of 6% for sentiment analysis on SenticNet. The second part of the unsupervised learning is in Chapter 5, introducing the IPA-based method for microtext normalization and sentiment analysis. Epitran was used to transform a word to its International Phonetic Alphabet (IPA) equivalent. The International Phonetic Alphabet (IPA) is an alphabetic system of phonetic notation based primarily on the Latin script. There is a significant improvement in microtext normalization task and sentiment analysis over Soundex. The results also show that there is very little redundancy when words are transformed to phonetics in IPA. In addition, the accuracy of polarity detection using this method has an improvement over baseline by 5%. Chapter 6 discusses a simple sequence-to-sequence based Deep Learning method. As the language is dynamic, it is not easy to maintain the lexicon. Deep Learning alleviates the static lexicon with a probabilistic model to transform microtext into its standard form. The findings suggest that a semiotic class of microtext was easy to transform, whereas the phonetic class of microtext was not handled easily. The proposed model improves the pre-trained sentiment analysis model by 4%. We also introduce a corpus for English microtext normalization. The corpus contains microtext-containing sentences along with their correctly spelt sentences and their correct polarity. This dataset can tackle two NLP problems, i.e., microtext normalization and sentiment analysis. This corpus consists of 38% positive, 39.9% negative and 22.1% neutral. A pre-trained model shows an accuracy of 46.4 % on OOV text for sentiment analysis, showing considerable room for improvement. In summary, this thesis introduces several approaches to microtext normalization analysis and focuses on sentiment analysis application. Microtext cannot be thought of as a one-dimensional problem to NLP. It encapsulates many factors like regional (geographical), ethnic (national and racial), and social (class, age, gender, socioeconomic status and education), which cannot be ignored given the growing importance of social media in our daily life.

A Review on Reddit News Headlines with NLTK tool

Chapter 20b - Estimating the growth or depreciation on exchange rates using sentiment analysis method from social media comments

Despite compelling evidence that statins are safe, are generally well tolerated, and reduce cardiovascular events, statins are underused even in patients with the highest risk. Social media may provide contemporary insights into public perceptions about statins. To characterize and classify public perceptions about statins that were gleaned from more than a decade of statin-related discussions on Reddit, a widely used social media platform. This qualitative study analyzed all statin-related discussions on the social media platform that were dated between January 1, 2009, and July 12, 2022. Statin- and cholesterol-focused communities, were identified to create a list of statin-related discussions. An artificial intelligence (AI) pipeline was developed to cluster these discussions into specific topics and overarching thematic groups. The pipeline consisted of a semisupervised natural language processing model (BERT [Bidirectional Encoder Representations from Transformers]), a dimensionality reduction technique, and a clustering algorithm. The sentiment for each discussion was labeled as positive, neutral, or negative using a pretrained BERT model. Statin-related posts and comments containing the terms statin and cholesterol. Statin-related topics and thematic groups. A total of 10 233 unique statin-related discussions (961 posts and 9272 comments) from 5188 unique authors were identified. The number of statin-related discussions increased by a mean (SD) of 32.9% (41.1%) per year. A total of 100 discussion topics were identified and were classified into 6 overarching thematic groups: (1) ketogenic diets, diabetes, supplements, and statins; (2) statin adverse effects; (3) statin hesitancy; (4) clinical trial appraisals; (5) pharmaceutical industry bias and statins; and (6) red yeast rice and statins. The sentiment analysis revealed that most discussions had a neutral (66.6%) or negative (30.8%) sentiment. Results of this study demonstrated the potential of an AI approach to analyze large, contemporary, publicly available social media data and generate insights into public perceptions about statins. This information may help guide strategies for addressing barriers to statin use and adherence.

PurposeDespite the extensive academic interest in social media sentiment for financial fields, multimodal data in the stock market has been neglected. The purpose of this paper is to explore the influence of multimodal social media data on stock performance, and investigate the underlying mechanism of two forms of social media data, i.e. text and pictures.Design/methodology/approachThis research employs panel vector autoregressive models to quantify the effect of the sentiment derived from two modalities in social media, i.e. text information and picture information. Through the models, the authors examine the short-term and long-term associations between social media sentiment and stock performance, measured by three metrics. Specifically, the authors design an enhanced sentiment analysis method, integrating random walk and word embeddings through Global Vectors for Word Representation (GloVe), to construct a domain-specific lexicon and apply it to textual sentiment analysis. Secondly, the authors exploit a deep learning framework based on convolutional neural networks to analyze the sentiment in picture data.FindingsThe empirical results derived from vector autoregressive models reveal that both measures of the sentiment extracted from textual information and pictorial information in social media are significant leading indicators of stock performance. Moreover, pictorial information and textual information have similar relationships with stock performance.Originality/valueTo the best of the authors’ knowledge, this is the first study that incorporates multimodal social media data for sentiment analysis, which is valuable in understanding pictures of social media data. The study offers significant implications for researchers and practitioners. This research informs researchers on the attention of multimodal social media data. The study’s findings provide some managerial recommendations, e.g. watching not only words but also pictures in social media.

The exponential growth of user-generated content on social media platforms (e.g., Twitter, Weibo, Facebook) has created massive datasets rich in public sentiment. While sentiment analysis proves vital for understanding social trends, brand perception, and political developments, traditional methods struggle to handle the informal nature, noise, and context-dependent characteristics of social media language. This necessitates advanced computational technologies to extract meaningful insights. This paper summarizes the application of Python in social media text sentiment analysis, covering various methods supported by its NLP library (NLTK, TextBlob), machine learning/deep learning framework (TensorFlow, PyTorch), and discusses cross-platform comparative analysis and specific domain adaptation. The results indicate that Python-based models achieve high levels of accuracy. For example, the RoBERTa-BiLSTM-MHA model achieved an accuracy of 93.44%, and these models outperform conventional tools by 612% in terms of F1 scores. Key findings reveal cultural and platform-based disparities in sentiment expression. Specifically, Chinese social media platforms like Weibo emphasize economic sentiment, while Western platforms such as Twitter focus more on technical and ethical implications. The integration of sentiment dictionaries and multimodal data, including emojis, further enhances the robustness of these sentiment analysis models. Overall, this review underscores Pythons versatility in enabling scalable and real-time sentiment analysis, which in turn drives innovations in NLP research and practical applications.

Toward reduction of detrimental effects of hurricanes using a social media data analytic Approach: How climate change is perceived?

Nowadays, with the increasing number and use of social media platforms, people now share their experiences about a product they have bought or a place they have been to on social media platforms more frequently. Considering the volume of data on social media platforms, it is considered that there is some meaningful information for institutions or companies in the reviews and experiences shared on social media platforms. As such, it is important to improve the methods of extracting meaningful information from the reviews and experiences shared on social media and to know which method is better. In this study, the classification successes of the bag of words and the fastText word representation methods, which are among the word representation methods in sentiment analysis methods mentioned above, were compared by using Turkish reviews performed for touristic places. Besides, while performing the comparison process, it was measured whether the process of separating the words into their roots and negation of the words, which is the preliminary stage of the sentiment analysis process, contributed to the classification success. In the study, both two-class (positive, negative) sentiment analysis and three-class (positive, negative, neutral) sentiment analysis were performed. Six data sets were created to carry out the mentioned comparison operations. The data sets were first classified using the Naive Bayes (NB), Multinomial Naive Bayes (MNB), k-Nearest Neighbor (k-NN) and Support Vector Machines (SVM) algorithms, which are frequently used in text mining, and based on bag of words word representation method, they were classified with WEKA program. After the test results of all data sets were obtained according to the bag of words word representation method, the tests of the fastText word representation method were carried out using the fastText library of the Python programming language. Classification procedures were carried out with 10-fold cross-validation methods, and f-score values of the classification processes were obtained. Finally, it was determined that bag of words word representation method performed a more successful classification than the fastText word representation method in two-class emotion analysis, while the fastText word representation method performed a more successful classification process than bag of words word representation method in three-class emotional analysis. It was observed that the process of separating the words into their roots and negating the words, which are the preliminary processes of sentiment analysis, did not contribute positively or negatively to the classification processes performed with the fastText word representation method. However, it was determined that it had a minor contribution to sentiment analysis processes performed by using bag of words word representation method. In the two-class sentiment analysis, the most successful classification result was achieved by using the machine learning model created with the SVM algorithm with the value of 0.91 f-score employing bag of words word representation method. In the three-class sentiment analysis, the most successful classification result was achieved with the machine learning model created using the fastText word representation method with the value of 0.78 f-score.

This research paper compiles and evaluates several studies that have a particular interest in social media analysis and its applications. Studies on evaluating the impact of movies and television shows on social media platforms, using semantic knowledge graphs to analyze Covid-19 news articles and identify fake news on social media, forecasting social media data using machine learning, and the evolution of the power of central nodes in a Twitter social network are all covered in this article. The significance of sentiment analysis and opinion mining in social media, analysis of social media-based profiles, and mining serendipitous drug use from social media using machine learning are all topics covered in the paper. The paper also emphasizes the application of social media text analysis for a high-frequency-link DC transformer based on switched capacitors for medium-voltage DC power distribution applications, and urban region mining services. The research paper offers details on the state of social media analysis right now and some potential uses for it.