GOOGLE PLAY STORE USERS COMMENT REVIEW CLASSIFICATION USING SVM CLASSIFIER AND RANDOM FOREST

Sentiment analysis, also known as opinion mining, has emerged as a pivotal field in the realm of social media. With the exponential growth of user-generated content on various platforms, understanding and extracting sentiments have become essential for businesses and organizations. In this research paper, we embark on a meticulous journey, delving into a comprehensive evaluation and comparative analysis of various data mining techniques employed for sentiment analysis in social media. The primary objective of this study is to provide practitioners and researchers with valuable insights into the strengths, limitations, and performance metrics of these techniques. By conducting an extensive evaluation, we aim to shed light on the effectiveness of different data mining approaches in capturing sentiments accurately and efficiently. Furthermore, we explore the challenges and limitations associated with sentiment analysis in social media, addressing the intricacies involved in analysing the vast and dynamic landscape of user-generated content

In today's digital age, an online reputation is a crucial asset for any business. Poorly managed responses to negative reviews on social media can lead to significant costs and damage. Sentiment analysis provides an effective way to monitor and analyze online opinions, particularly in real-time, allowing businesses to track public sentiment regarding their products and services. This project leverages sentiment analysis on Twitter to harness the power of real-time data, enabling businesses to assess and respond to customer feedback promptly. By using Long Short-Term Memory (LSTM) models, this approach offers advanced capabilities in analyzing tweet sentiments, providing a deeper understanding of consumer sentiment and enhancing social media monitoring.One key improvement of this project over existing tools is the focused collection of data exclusively from Twitter, reducing noise and minimizing the risk of false results caused by irrelevant data sources. By analyzing user interactions on social media, beyond basic metrics like likes, shares, and comments, sentiment analysis seeks to uncover the underlying emotions and motivations of consumers, providing valuable insights for brands, public figures, NGOs, governments, and educational institutions.Existing sentiment analysis tools typically require a background in data science and advanced technical knowledge. However, this project introduces a user-friendly interface, allowing non-experts to easily access and interpret sentiment analysis results. The interface will display product reviews along with their corresponding sentiments, providing a seamless experience for the user. Additionally, the project incorporates a phrase-level sentiment analysis feature, which analyzes user-input phrases and predicts the sentiment behind them, offering a more granular and precise understanding of social media content.

Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution

In the digital age, social media has become a transformative force, reshaping consumer interactions with products, brands, and services. This essay delves into the intricate interplay between cultural factors and consumer behavior within the realm of social media, focusing on a cross-cultural comparative study between Japan and the United States. The study explores consumer behavior through the lens of psychological, social, and cultural influences. It investigates how psychological aspects like motivation and attitudes, as well as social factors such as family and reference groups, are intertwined with cultural dimensions to shape consumer preferences. This essay employs a comparative approach, focusing on Japan and the United States as distinct cultural contexts. By applying Hofstede's cultural dimensions model, it dissects differences in individualism, power distance, masculinity-femininity, long-term orientation, uncertainty avoidance, and indulgence-restraint. The findings reveal significant disparities in consumer behavior on social media between these two nations. Cultural factors profoundly impact user engagement, content resonance, and purchasing decisions on social media platforms. This study underscores the indispensable role of cultural factors in molding consumer behavior in the digital age. Understanding and adapting to cultural nuances are imperative for marketers seeking to effectively engage and influence diverse global audiences through social media channels.

Sentiment Analysis (SA) has gained its popularity over the years for the benefit it brings to the development of economy, sociology and politic. SA enables observation, experiment, and quantification of emotions of the public toward a particular issue. However, there is not much SA done with respect to the Malay Language, especially in the context of the Malay dialects used in social media. The research presented in this paper aims to perform SA on one of the derivatives of the Malay language, namely Sabah Language. The Sabah Language, unlike many other languages, does not have a fixed spelling and, when used in an unstructured form as in the case of social media, poses particular difficulties for SA. This paper takes a lexicon-based approach to SA of the Sabah Language as used on social media. For the investigation, the corpuses selected were Facebook posts and tweets written in the Sabah language, 443 posts and tweets in total. Each was manually annotated as positive, negative or neutral by three annotators. As Sabah Language is a derivative of Malay language, the words used in Sabah Language contains most of Malay words. That is why, in Sentiment-Lexicon (SL) construction process, opinion-bearing Malay SL is retrieved, modified and expanded to build Sabah SL. Three different methods of assigning scores to the words in SL (opinion-bearing words) were employed during SL construction: (i) Simple PSA, (ii) Simple PSA with Switch Negation (PSA-SN) and (iii) Strength-based PSA. In this paper, pre-processing phase that includes spellchecker and shortform corrector is also implemented to reduce distinct word to be analyzed for SA. In classification phase, two classification methods, simple and bias aware classifications, were used to classify the posts. Experiments are conducted to show the effect of SL modification and expansion, the effect of pre-processing as well as the effect of bias-aware classification to the SA performed. Results show the highest accuracy of 85.10% was achieved using bias-aware classification with the modified and expanded SL, scores are assigned using Simple PSA and the pre-processed text.

In the digital era, social media has emerged as a powerful marketing tool, revolutionizing the way businesses interact with consumers. As social media platforms continue to evolve, it is crucial for marketers to effectively measure and analyze their efforts. This article explores the significance of marketing metrics in the realm of social media, highlighting the key metrics used to evaluate marketing campaigns and their impact on business success. Through an in-depth analysis of various marketing metrics, this article provides insights into optimizing social media strategies for enhanced customer engagement, brand awareness, and return on investment. The primary objective of this article is to delve into the significance of marketing metrics in the realm of social media. By examining various marketing metrics and their relevance in evaluating social media campaigns, this article aims to provide marketers with insights on how to optimize their strategies and achieve desired outcomes. The scope of this article encompasses a comprehensive analysis of key marketing metrics, including reach and impressions, engagement metrics, conversion metrics, customer satisfaction metrics, brand awareness metrics, and influence and authority metrics. This article is based on a thorough review of existing literature, industry reports, case studies, and expert opinions on marketing metrics in social media. Primary and secondary sources were consulted to gather information on the various metrics used in social media marketing and their impact on business success. The analysis and insights presented in this article are derived from a synthesis of the available knowledge and experiences shared by marketing professionals and researchers in the field of social media marketing. By exploring the importance of marketing metrics in the context of social media, this article aims to equip marketers with the necessary knowledge and tools to make data-driven decisions, optimize their social media strategies, and achieve meaningful results.

Social networking platforms have become an essential means for communicating feelings to the entire world due to rapid expansion in the Internet era. Several people use textual content, pictures, audio, and video to express their feelings or viewpoints. Text communication via Web-based networking media, on the other hand, is somewhat overwhelming. Every second, a massive amount of unstructured data is generated on the Internet due to social media platforms. The data must be processed as rapidly as generated to comprehend human psychology, and it can be accomplished using sentiment analysis, which recognizes polarity in texts. It assesses whether the author has a negative, positive, or neutral attitude toward an item, administration, individual, or location. In some applications, sentiment analysis is insufficient and hence requires emotion detection, which determines an individual’s emotional/mental state precisely. This review paper provides understanding into levels of sentiment analysis, various emotion models, and the process of sentiment analysis and emotion detection from text. Finally, this paper discusses the challenges faced during sentiment and emotion analysis.

In an increasingly complex digital era, sentiment analysis has become a vital instrument in understanding the nuances of public opinion. This technique, which utilizes artificial intelligence and Machine Learning, allows us to extract knowledge about people's attitudes, emotions and perceptions towards various issues. This research examines public sentiment regarding the issue of fraud in the 2024 Election on the social media platform Twitter using a text mining-based sentiment analysis approach. Data was obtained through a crawling process using the Python programming language. The research methodology includes a series of stages, starting from data cleaning to improve quality, continuing with word weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm, and ending with modeling using the Naïve Bayes Classifier algorithm. Model evaluation was carried out systematically by applying the Naive Bayes, Confusion Matrix and K-Fold Cross Validation methods to measure the level of accuracy and effectiveness of the model developed. This research aims to produce in-depth knowledge regarding the trends and dynamics of public sentiment regarding the issue of fraud in the 2024 Election in the realm of social media, especially Twitter (X). Based on the research results, it shows a percentage of 67.7%.

Using social media effectively in a surgical practice

We study the interaction of short sellers and social media and the effect on stock prices. We use 75.1 million investment-related social media posts for 3,683 unique Chinese firms. Prior to high short interest, social media tone is abnormally positive. Once highly shorted, the tone flips and is abnormally negative. No such pattern exists with traditional media. Compared to firms that are just highly shorted, highly shorted firms with pump-and-dump patterns in social media tone have abnormal returns that are 2.7x higher before, and 3x lower after, the initiation of high short interest. Evidence from natural experiments involving China’s introduction and subsequent suspension of shorting also suggest social media manipulation. Manipulation is more likely in firms located in provinces with weaker legal environments. Our findings show that in the realm of social media, short sellers may profit more by creating mispricing than by correcting it.

BackgroundIn the digital age, social media has become a crucial platform for public discourse on diverse health-related topics, including vaccines. Efficient sentiment analysis and hesitancy detection are essential for understanding public opinions and concerns. Large language models (LLMs) offer advanced capabilities for processing complex linguistic patterns, potentially providing valuable insights into vaccine-related discourse.ObjectiveThis study aims to evaluate the performance of various LLMs in sentiment analysis and hesitancy detection related to vaccine discussions on social media and identify the most efficient, accurate, and cost-effective model for detecting vaccine-related public sentiment and hesitancy trends.MethodsWe used several LLMs—generative pretrained transformer (GPT-3.5), GPT-4, Claude-3 Sonnet, and Llama 2—to process and classify complex linguistic data related to human papillomavirus; measles, mumps, and rubella; and vaccines overall from X (formerly known as Twitter), Reddit, and YouTube. The models were tested across different learning paradigms: zero-shot, 1-shot, and few-shot to determine their adaptability and learning efficiency with varying amounts of training data. We evaluated the models’ performance using accuracy, F1-score, precision, and recall. In addition, we conducted a cost analysis focused on token usage to assess the computational efficiency of each approach.ResultsGPT-4 (F1-score=0.85 and accuracy=0.83) outperformed GPT-3.5, Llama 2, and Claude-3 Sonnet across various metrics, regardless of the sentiment type or learning paradigm. Few-shot learning did not significantly enhance performance compared with the zero-shot paradigm. Moreover, the increased computational costs and token usage associated with few-shot learning did not justify its application, given the marginal improvement in model performance. The analysis highlighted challenges in classifying neutral sentiments and convenience, correctly interpreting sarcasm, and accurately identifying indirect expressions of vaccine hesitancy, emphasizing the need for model refinement.ConclusionsGPT-4 emerged as the most accurate model, excelling in sentiment and hesitancy analysis. Performance differences between learning paradigms were minimal, making zero-shot learning preferable for its balance of accuracy and computational efficiency. However, the zero-shot GPT-4 model is not the most cost-effective compared with traditional machine learning. A hybrid approach, using LLMs for initial annotation and traditional models for training, could optimize cost and performance. Despite reliance on specific LLM versions and a limited focus on certain vaccine types and platforms, our findings underscore the capabilities and limitations of LLMs in vaccine sentiment and hesitancy analysis, highlighting the need for ongoing evaluation and adaptation in public health communication strategies.

The article presents an analysis of the problems that professional wrestlers face in their utilization of social media and the various strategies they employ in order to create a successful cohesion between the identity they present on the ring and their social media presence. Because of the metaphysical split that lies in the very foundation of wrestling the wrestler exists in two different realities—the world of everyday ordinary life on one side and the world of kayfabe on the other. The consequences from that grow in importance with the transition of wrestling into a televised form of entertainment and the conflict becomes even more emphasized when wrestling comes in contact with the realm of social media. The wrestler may choose to avoid social media altogether or she may choose to utilize social media as a continuation of her in-ring persona, or she may choose to initiate an interaction between the reality spheres of social media and wrestling. In the second part, I examine the challenges that the wrestling promotions face in their attempts to create a benign and engaging corporate identity. Historically wrestling has oftentimes exploited various negative stereotypes related to gender and race and this heritage continues to haunt the promotions up to this day. The contemporary problems lay in the field of social justice and the cruel ways in which the promotions treat their workers—the lack of permanent contracts, the uncertainty about health insurance and the attempts to ban wrestlers from utilizing social media.

Background: Data is rapidly expanding in today's digital age. The reason for the expansion of data is due i to social media sites. The Internet produces an enormous quantity of unstructured data every second. Numerous users have many opinions and reviews to impart on everything from items and services to common pastimes. Opinions, feelings, attitudes, impressions, etc., concerning subjects, products, and services are collected and analyzed through a method called sentiment analysis. Web-based networking mediums that rely on textual communication can be overwhelming. Understanding human psychology requires the real-time processing of data using techniques like sentiment analysis. Aim: This study provides a thorough examination of the differences between methods of sentiment analysis, as well as its obstacles and emerging trends. The paper exemplifies the analysis's practical uses, examines its challenges, and outlines common methods of conducting it. Objective: The objective of the current overview is to better understand the market, gauge public opinion, and make strategic decisions. In addition, enterprises, governments, and scholars can all benefit from conducting a sentiment analysis. Methods: In this research, we review and categorize the most widely applied methods of deep learning and machine learning for analyzing sentiment. From the paper, we learn that which sentiment analysis technique is the best depends on the data at hand. When confronted with large amounts of data and a lengthy procedure, traditional machine learning-based algorithms flop. The ability to train deep learning models to learn more features using larger datasets is why they currently beat machine learning methodologies. Considerations include textual and temporal context, as well as data volume. Results: Regardless of the fact that the English language has traditionally been the focus of research in this field, other spoken languages have recently attracted a growing amount of interest. The lack of resources for these languages continues to present numerous obstacles. Consequently, it can be an intriguing line of future effort to tackle other natural languages outside English by generating beneficial resources like building databases and addressing the problems with Natural language processing that have been stated in the context of sentiment examination. Conclusion: The difficulties of sentiment analysis are examined as well, with the goal of illuminating potential solutions.

In today’s society, online marketing has become essential for almost every business. As digitalization accelerates, companies are increasingly compelled to strengthen their online presence and strategically position themselves within the digital space. Younger consumers, in particular, actively use social media platforms to inform their purchasing decisions. As a result, it is critical for brands to boost their visibility on these platforms and develop effective marketing strategies. With consumers becoming more mobile, they expect personalized experiences and seamless interactions across all touchpoints when engaging with a company. To address these challenges, the integration of artificial intelligence (AI) in marketing, alongside collaborations with influencers, has gained significant traction in recent years. AI plays a pivotal role in crafting personalized customer experiences, while influencers have considerable sway over young consumers. Social media campaigns are especially effective in shaping the consumption habits of adolescents, who tend to trust the recommendations and experiences shared by influencers. This trust significantly influences their purchasing behavior. In particular, influencer marketing strategies on platforms like Instagram enable brands to effectively connect with younger audiences. This study aims to explore how businesses utilize AI to enhance their online marketing efforts within the realm of social media, and to analyze the influence of influencers on the consumption patterns of young people. In this context, the growing impact of influencer marketing is increasingly shaping brand loyalty among young consumers, leaving a lasting effect in today’s digital age.

Transgender individuals encounter unique challenges in the digital age, particularly concerning social media and artificial intelligence (AI) tools. This article delves into the multifaceted issues faced by the transgender community, including algorithmic biases, content moderation disparities, and the perpetuation of harmful stereotypes. By examining current literature and real-world examples, the study highlights the systemic obstacles that hinder equitable digital experiences for transgender users. The research employs a qualitative methodology, analysing data from various studies and reports to understand the landscape comprehensively. Findings indicate that while social media platforms offer spaces for community building and support, they also harbour environments where AI-driven systems can inadvertently marginalise transgender voices. The article recommends for creating more inclusive digital platforms, emphasising the need for diverse training data, inclusive algorithm design, and active involvement of transgender individuals in developing AI tools.