Sentiment Analysis Algorithms Research Articles

Twitter Sentiment Analysis Using Machine Learning

In an age of social media, online forums, and chats, cyberbullying is a prevalent issue. On Twitter (now X), approximately 500 million tweets are shared per day (Antonakaki et.al., 2021). It is the job of the moderators to ensure these tweets follow standard community guidelines. However, the sheer number of tweets makes it difficult to sort manually and ensure they are following protocol. Sentiment analysis and machine learning algorithms can be used to classify these texts automatically as positive or negative. Normally, these machine learning models are much more efficient and may provide higher accuracy rates in identifying hate speech in Twitter. In this paper, we are exploring the use of five classical machine learning algorithms to classify Twitter hate speech as neutral, racist, or sexist. Model performance was compared after using raw tweet data versus pre-processed tweets through data cleanup. Furthermore, we highlight two methods to deal with imbalanced datasets to improve the prediction rates. Overall, we were able to achieve a 96% accuracy in correctly classifying tweets into the different labels.

Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News

These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model's prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.

Correlation Between News and Stock Price Based on Stock Market Indices: Can News Classification Be Used as a Tool to Make Better Decisions?

The stock market is influenced by various factors, including news events, economic indicators, and investor sentiment. Understanding the correlation between news and stock price movements interests market participants and researchers. In this paper, we explore the relationship between news sentiment and stock market trends using stock market indices. We employ natural language processing (NLP) techniques to classify news articles and analyze their impact on stock market indices, focusing on the S&P 500 and the Dow Jones Industrial Average. We utilize sentiment analysis and machine learning algorithms, including Random Forest, Loughran-McDonald (2014) Financial Sentiment Word Lists (Extended), and AFINN Lexicon, to predict stock market trends based on news sentiment. Our findings demonstrate that positive news sentiment has a more significant impact on stock prices than negative sentiment. The Random Forest model achieves the highest accuracy, while domain-specific lexicons provide valuable insights into financial news sentiment. However, predicting negative trends remains a challenge across all methods. Our research contributes to the growing knowledge of the relationship between news and stock prices and provides valuable insights for market participants and researchers.

Deep learning in finance assessing twitter sentiment impact and prediction on stocks

The widespread adoption of social media platforms has led to an influx of data that reflects public sentiment, presenting a novel opportunity for market analysis. This research aims to quantify the correlation between the fleeting sentiments expressed on social media and the measurable fluctuations in the stock market. By adapting a pre-existing sentiment analysis algorithm, we refined a model specifically for evaluating the sentiment of tweets associated with financial markets. The model was trained and validated against a comprehensive dataset of stock-related discussions on Twitter, allowing for the identification of subtle emotional cues that may predict changes in stock prices. Our quantitative approach and methodical testing have revealed a statistically significant relationship between sentiment expressed on Twitter and subsequent stock market activity. These findings suggest that machine learning algorithms can be instrumental in enhancing the analytical capabilities of financial experts. This article details the technical methodologies used, the obstacles overcome, and the potential benefits of integrating machine learning-based sentiment analysis into the realm of economic forecasting.

An Interview System Using AI Technology

The rapid advancement of artificial intelligence (AI) has led to innovative applications across various industries, and one such application is the development of an AI-based interview system. This system aims to enhance and streamline the traditional interview process by leveraging AI technologies to assess and interact with candidates. The AI- based interview system incorporates natural language processing (NLP), machine learning (ML), and sentiment analysis algorithms to analyze both verbal and non-verbal cues during interviews. By employing AI, the system can assess a candidate's communication skills, problem-solving abilities, and cultural fit within the organization. The system's machine learning algorithms continuously learn and improve over time, enhancing their ability to accurately evaluate candidates and predict their potential success within the company. The benefits of an AI-based interview system include increased efficiency in the hiring process, reduction in human bias, and the ability to handle a large number of interviews simultaneously. Furthermore, the system can provide data-driven insights to recruiters, aiding in evidence-based decision-making. While the development and implementation of an AI-based interview system pose ethical considerations and challenges related to privacy and bias, ongoing research and ethical guidelines are essential to address these concerns and ensure fair and transparent use of the technology. As technology continues to evolve, further research and collaboration between AI developers and human resource professionals are crucial to harness the full potential of this innovative tool in the hiring process.

Harnessing Machine Learning to Unveil Emotional Responses to Hateful Content on Social Media

Within the dynamic realm of social media, the proliferation of harmful content can significantly influence user engagement and emotional health. This study presents an in-depth analysis that bridges diverse domains, from examining the aftereffects of personal online attacks to the intricacies of online trolling. By leveraging an AI-driven framework, we systematically implemented high-precision attack detection, psycholinguistic feature extraction, and sentiment analysis algorithms, each tailored to the unique linguistic contexts found within user-generated content on platforms like Reddit. Our dataset, which spans a comprehensive spectrum of social media interactions, underwent rigorous analysis employing classical statistical methods, Bayesian estimation, and model-theoretic analysis. This multi-pronged methodological approach allowed us to chart the complex emotional responses of users subjected to online negativity, covering a spectrum from harassment and cyberbullying to subtle forms of trolling. Empirical results from our study reveal a clear dose–response effect; personal attacks are quantifiably linked to declines in user activity, with our data indicating a 5% reduction after 1–2 attacks, 15% after 3–5 attacks, and 25% after 6–10 attacks, demonstrating the significant deterring effect of such negative encounters. Moreover, sentiment analysis unveiled the intricate emotional reactions users have to these interactions, further emphasizing the potential for AI-driven methodologies to promote more inclusive and supportive digital communities. This research underscores the critical need for interdisciplinary approaches in understanding social media’s complex dynamics and sheds light on significant insights relevant to the development of regulation policies, the formation of community guidelines, and the creation of AI tools tailored to detect and counteract harmful content. The goal is to mitigate the impact of such content on user emotions and ensure the healthy engagement of users in online spaces.

Active Chat Monitoring and Suspicious Chat Detection Over Internet

In today's digital landscape, the proliferation of online communication platforms has brought about unprecedented opportunities for interaction and collaboration. However, this vast virtual space also presents challenges in ensuring safety and security, particularly concerning the monitoring of chat activities and the detection of suspicious behaviour. This research paper investigates the methods and technologies employed proactive chat monitoring and random chat across the web. The paper begins by examining the importance of monitoring chat platforms to prevent various forms of malicious activities, including cyberbullying, fraud, terrorism, and other illicit behaviours. It then delves into the techniques utilized for real-time monitoring of chat conversations, such as keyword filtering, sentiment analysis, and natural language processing algorithms. Furthermore, the paper explores the principles behind suspicious chat detection, highlighting the indicators of potentially harmful or illegal activities within chat communications. This includes analysing patterns of conversation, identifying anomalous behaviour, and integrating machine learning models to improve detection accuracy. Moreover, the research discusses the ethical considerations and privacy concerns associated with active chat monitoring, emphasizing the need for transparent policies and safeguards to protect user privacy while ensuring effective security measures. Keywords: Active chat monitoring, Suspicious chat detection, Online communication platforms, Cybersecurity, Malicious activities, Keyword filtering, Sentiment analysis, Natural language processing algorithms.

Sentiment Analysis on Placement Aspect

Sentiment analysis has emerged as a pivotal tool within the realm of natural language processing, finding widespread application across diverse domains such as marketing, social media analysis, and customer feedback assessment. This research delves into the specialized domain of sentiment analysis focused on the placement aspect, which revolves around the intricate process of aligning individuals with suitable job opportunities. The primary objective of this study is to dissect sentiments embedded within textual data associated with placements, including resumes, job listings, and interview appraisals, with the aim of extracting nuanced insights to inform strategic decision-making within recruitment and talent acquisition processes. Leveraging cutting-edge techniques in machine learning and natural language processing, our research employs a multifaceted approach encompassing text preprocessing, feature extraction, and sentiment classification methodologies. A spectrum of sentiment analysis algorithms is explored, ranging from lexicon-based methods to sophisticated machine learning models (such as Support Vector Machines and Naive Bayes) and deep learning architectures (including Recurrent Neural Networks and Transformers). Additionally, our investigation extends to assessing the influence of various factors—such as dataset dimensions, domain specificity, and feature selection techniques—on the accuracy and robustness of sentiment analysis outcomes.

Twitter Sentiment Analysis using Python (TweetX)

Twitter has become a significant platform for expressing opinions and emotions on a wide range of topics. This study presents a comprehensive analysis of sentiment expressed in Twitter data using natural language processing and machine learning techniques. The primary objective is to gauge public sentiment on various subjects, from politics to brand perception, by analysing tweets. We collected a vast dataset of tweets, pre-processed the text, and employed sentiment analysis algorithms to classify tweets as positive, negative, or neutral. The results provide valuable insights into public sentiment trends, allowing businesses, policymakers, and researchers to make informed decisions based on the collective voice of Twitter users. The study demonstrates the potential of sentiment analysis as a tool for understanding public sentiment and monitoring real-time public opinion Key Words: (NLTK), TextBlob

Applying advanced sentiment analysis for strategic marketing insights: A case study of BBVA using machine learning techniques

In the digital era, understanding public sentiment toward brands on social media is essential for crafting effective marketing strategies. This study applies sentiment analysis on Banco Bilbao Vizcaya Argentaria (BBVA) tweets using advanced machine learning techniques, particularly the eXtreme Gradient Boosting (XGBoost) algorithm, which showed remarkable precision (91.2%) in sentiment classification. This process involved a systematic approach to data collection, cleaning, and preprocessing. The precision of XGBoost highlights its effectiveness in analyzing social media conversations about banking. Additionally, this paper achieved improvements in neutral tweet classification, with accuracy rates at 87-88% and a reduced misclassification rate, enhancing the analysis reliability. The findings not only uncover general sentiments toward BBVA but also provide insight into how these sentiments shift in response to marketing activities and global events. This gives marketers a valuable tool for real-time assessment of campaign effectiveness and brand perception. Ultimately, employing the XGBoost algorithm for sentiment analysis offers BBVA a strategic advantage in understanding and engaging its online audience, demonstrating the significant benefits of using sophisticated machine learning in banking. The study emphasizes the crucial role of data-driven sentiment analysis in developing informed business strategies and improving customer relationships in the banking industry’s competitive landscape.

Enhancing Financial Sentiment Analysis: A Deep Dive into Natural Language Processing for Market Prediction Industries

The purpose of this study is to investigate the enhancement of Financial Sentiment Analysis by conducting an in-depth investigation of Natural Language Processing (NLP) approaches for the purpose of improving market prediction. The purpose of this research is to investigate the potential of natural language processing (NLP) to improve the accuracy and efficiency of sentiment analysis. This is in response to the complex structure of financial markets and the crucial role that sentiment plays. The examination of the relevant literature highlights the limits of traditional methods and the urgent need for creative solutions in the field of financial sentiment research. The approach that we use entails the careful collecting of data from social media and financial news, with a particular emphasis on the utilization of strong pre-processing tools. The research assesses the performance parameters of accuracy, precision, recall, and correlation with market trends by using natural language processing (NLP) technologies such as algorithms for sentiment analysis, Named Entity Recognition, and deep learning models. The findings include a comparative examination of conventional methods and those based on natural language processing (NLP), therefore revealing insights into the significant influence that sentiment has on market patterns. The results not only provide a contribution to the theoretical knowledge of sentiment research, but they also offer real consequences for financial analysts who are looking to make market forecasts that are more accurate and timelier. The research suggests ways for refinement, with an emphasis on enhanced pre-processing and Explainable AI integration. These tactics are being proposed to address issues in data quality and bias. When looking to the future, the study provides an overview of potential future paths, which include the investigation of external influences and the development of deep learning models for accurate market forecasting respectively. To summaries, the findings of this research establish natural language processing (NLP) as a revolutionary force in the process of redefining financial sentiment analysis. Furthermore, it offers a path for future developments in the ever-changing world of market prediction.

Comparison of novel character relationship network mining and drama character relationship shaping algorithms

This study embarks on an interdisciplinary journey to analyze and compare Character Relationships (CR) in two diverse storytelling mediums – the classic novel “Emma” by Jane Austen and the popular TV series “Friends”. Leveraging a blend of Natural Language Processing (NLP) and advanced video analysis tools, this research “sheds light on” the intricate network of CR within these narratives. This study scientifically analyzes these relationships' complete method, creation, and impact using sentiment analysis, object identification, and story coherence algorithms. Qualitative and quantitative metrics such as precision, recall, F-score, and accuracy assist in explaining character updates. The content and TV exhibit distinct storytelling modes, and this study demonstrates that algorithmic analysis of stories is practical. The findings of this study request to contribute to online studies by focusing on understanding character networks and their vital role in TV storytelling.

Intelligent Analysis of E-commerce Reviews based on Complex-Valued Spatio-temporal Graph Convolutional Neural Network Optimized Tyrannosaurus Optimization Algorithm

Intelligent analysis of E-commerce reviews based on multimodal social media big data using applies the benefit of innovative artificial intelligence method, especially deep learning algorithms, to study and realize web-based product consider and social network data connected to e-commerce. In this manuscript, Intelligent Analysis of E-commerce reviews based on Complex-Valued Spatio-temporal Graph Convolutional Neural Network Optimized Tyrannosaurus Optimization Algorithm (IAER-CVSGCNN-TOA)is proposed for Intelligent Analysis of E-commerce Reviews. The input data are collected from E-commerce big Dataset. Then data are pre-processing utilizing federated neural collaborative filtering (FNCF) to remove the noise and clean the data. The pre-processed data is provided to the CVSGCNN is used for intelligent analysis of e-commerce reviews. In general, CVSGCNN does no express adapting optimization approaches to determine optimal parameters to ensure accurate prediction. Hence, proposed to utilize the Tyrannosaurus Optimization Algorithm enhancement CVSGCNN for intelligent analysis of e-commerce reviews. The proposed IAER-CVSGCNN-TOA method is implemented on python. Then performance of proposed technique is analyzed with other existing techniques. The proposed technique attains 30.26%, 28.70% and 46.25% higher accuracy, 20.53%, 32.60%, and 53.43% higher precision, 25.23%, 23.15%, and 22.33%higher recall, 25.75%, 44.30% and 53.70%higher specificity comparing with the existing methods such as a Retracted: Online Troll Reviewer Detection Utilizing Deep Learning Methods (IAER-CNN), Developing an Intelligent System and DL Algorithms for Sentiment Analysis of E-Commerce Product Reviews(IAER-LSTM), Intelligent Perception System of Big Data Decision in Cross-Border e-Commerce Depend on Data Fusion(IAER-DFN) respectively.

SKILL SYNC SELECTOR USING MACHINE LEARNING TECHNIQUES

Our study introduces an Automatic Resume Scanner Application that makes an advantage of advanced natural language processing (NLP). Together with performing thorough linguistic analysis and effectively standardizing resumes, it leverages state-of-the-art natural language processing (NLP) algorithms for sentiment analysis, context interpretation, and profile ranking. Using machine learning algorithms, it matches candidate qualifications with job demands through an intuitive interface that allows criteria to be customized. The application additionally manages bias detection and reduction in order to advance equity and inclusivity in the hiring process. When everything is said and done, it provides a fast, accurate, and neutral way to screen candidates at a preliminary stage, saving time and fostering a diversity of ability. Key Words: Resume screening, machine learning, Natural Language Processing(NLP), Talent acquisition,Text mining, KNeighbors Classifier, OnevsRest Classifier, TF-IDF vectorization, Label encoding, Feature engineering.

SENTI Aid: Sentiment Analysis on Personal Relations and Aiding Mental Health

This research examined the ability of a novel mobile application designed to provide proactive mental health support by analyzing the user’s conversations and recommends interventions accordingly. Employing sentiment analysis of the user's recorded discussions with designated social contacts (parents, siblings, partner), the application identifies indicators of potential issues in mental health. A personalized chatbot then interacts with the user, offering feedback based on the sentiment analysis and engages in positive conversation to uplift user’s mood. Additionally, the system monitors the user's application activities and chatbot interaction patterns, detecting atypical behaviors for further feedback or prompting emergency alerts to pre-defined contacts. The research employed a two-phased approach: an initial pilot study with simulated data to refine the sentiment analysis and chatbot algorithms, followed by a validation study with a limited user group, utilizing actual conversation recordings. Analysis of the pilot data showed promising accuracy in identifying negative sentiments, while the validation study demonstrated a significant improvement in positive engagement and self- reported well-being among participants. Overall, the findings suggest that this multi-faceted approach using sentiment analysis and conversational AI holds potential for early detection and proactive intervention in mental health issues, justifying further investigation and refinement for broader implementation.

Comparison of Naïve Bayes and SVM in Sentiment Analysis of Product Reviews on Marketplaces

At this time more and more people are switching to shopping online in existing marketplaces such as Shopee. Marketplaces provide various advantages and disadvantages to customers such as lower costs and goods sent not according to orders. Product reviews from customers greatly affect the sales level of business people so that sentiment analysis is carried out. The importance of conducting sentiment analysis of product reviews in the marketplace is to add an overview of how the product is received by users. This research uses Naïve Bayes and SVM algorithms for sentiment analysis of beauty care product review datasets obtained from Shopee scraping results. This research implements k fold cross validation for data splitting process of 10 folds. The Naïve Bayes algorithm obtained the highest accuracy value of 85.53% on fold 2 and the lowest accuracy value of 77.16% on fold 3. While the SVM algorithm obtained the highest accuracy value of 88.58% on fold 2 and the lowest accuracy value of 82.99% on fold 7. With this it is stated that SVM can work better for sentiment analysis of beauty care product reviews on the Shopee marketplace because it gets a higher average accuracy value of 86.14% compared to the Naïve Bayes algorithm.

Exploring Stress Detection in Tweets: A Comparative Business Analysis of Classification Algorithms

Abstract: In today's digitally-driven landscape, social media platforms such as Twitter, Facebook, and WordPress have emerged as critical conduits for public discourse, resulting in an overwhelming influx of unprocessed data, particularly on Twitter. Coping with the sheer volume of this data presents a significant challenge. To address this, sentiment analysis, a technique that categorizes sentiments into positive, negative, or neutral, offers a promising solution. This study delves into three main approaches for sentiment analysis: Machine learning-based methods, Sentiment lexicon-based approaches, and Hybrid methods. Its primary research objectives revolve around the identification of suitable algorithms and metrics for evaluating the performance of Machine Learning Classifiers. Additionally, the study aims to compare these metrics with respect to the dataset size, gauging their impact on the most appropriate sentiment analysis algorithm.The research methodology applied is experimental, entailing a rigorous assessment of algorithms using carefully selected metrics. The results of this investigation spotlight Naïve Bayes, Random Forest, XGBoost, and CNN-LSTM as the leading machine learning algorithms under consideration. These algorithms are assessed based on key performance metrics such as precision, accuracy, F1 score, and recall. Notably, the CNN-LSTM model emerges as the optimal choice for sentiment analysis of Twitter data within the specified dataset size, achieving a remarkable accuracy rate of 88%.In summary, this research successfully pinpoints the most suitable algorithm for sentiment analysis of Twitter data, especially in the context of dataset size. The CNN- LSTM model showcases its effectiveness, serving as a robust tool for sentiment analysis and delivering an impressive accuracy rate. This study significantly enhances our comprehension of public sentiment on Twitter, providing valuable insights into the ever-evolving realm of digital discourse and the analysis of vast unprocessed data

Leveraging spark-based machine learning algorithm for audience sentiment analysis in youtube content

In today’s digital era, the generation and sharing of information are rapidly expanding. The increased volume of complex data is big data. YouTube is the primary source of big data. The proliferation of the internet and smart devices has led to a significant increase in content creators on social media platforms, with YouTube being a prominent example. There has been a substantial increase in content creators across various social media platforms, with YouTube emerging as one of the foremost platforms for content generation and sharing. YouTubers face challenges in enhancing content strategies due to the growing number of comments, such as big data on shared videos. Reading and finding viewers’ opinions of such a large amount of data through manual methods is time-consuming and challenging and makes it hard to understand people’s sentiments. To address this, spark-based machine learning algorithms have emerged as a transformative tool for content creators to understand the audience. The Improved Novel Ensemble Method (INEM) algorithm is designed to predict viewers’ sentiments and emotional responses based on the content they interact through the comments. The proposed results provide valuable insights for content creators, helping them refine the strategies to optimize the channel’s revenue and performance. Fit Tuber Channel is analyzed to perform the sentiment of user comments.

GP‐FMLNet: A feature matrix learning network enhanced by glyph and phonetic information for Chinese sentiment analysis

AbstractSentiment analysis is a fine‐grained analysis task that aims to identify the sentiment polarity of a specified sentence. Existing methods in Chinese sentiment analysis tasks only consider sentiment features from a single pole and scale and thus cannot fully exploit and utilise sentiment feature information, making their performance less than ideal. To resolve the problem, the authors propose a new method, GP‐FMLNet, that integrates both glyph and phonetic information and design a novel feature matrix learning process for phonetic features with which to model words that have the same pinyin information but different glyph information. Our method solves the problem of misspelling words influencing sentiment polarity prediction results. Specifically, the authors iteratively mine character, glyph, and pinyin features from the input comments sentences. Then, the authors use soft attention and matrix compound modules to model the phonetic features, which empowers their model to keep on zeroing in on the dynamic‐setting words in various positions and to dispense with the impacts of the deceptive‐setting ones. Experiments on six public datasets prove that the proposed model fully utilises the glyph and phonetic information and improves on the performance of existing Chinese sentiment analysis algorithms.

Modified firefly algorithm and different approaches for sentiment analysis

The firefly algorithm is the tool of swarm intelligence in almost all areas of optimization and engineering practice. Firefly algorithm and its variants have been used to solve diverse problems of real-world and motivate new researchers and algorithm developers to use this simple algorithm for problem-solving. The population of the initial solution, firefly light intensity rank selection and position update are important parameters of the firefly algorithm. The optimization result will vary in the variation of type of function selection by the researchers and developers. Therefore, here an effort has been made to propose a hybrid firefly algorithm where a population of initial solution generation done by the differential evolution algorithm (DEA) and selection of light intensity and change in position of firefly position will be done by position updating techniques of PSO. The hybrid firefly algorithm has been tested on five benchmark data set with nine benchmark algorithms and experiment result showed that firefly outperformed the other used algorithm. The aim of this study is also to motivate researchers to apply this algorithm for sentiment analysis also.