Twitter Sentiment Analysis Research Articles

Sentiment Analysis and Marketing Mix in Twitter Conversations About Mixue

This study delves into the rapid growth and public reception of Mixue, a Chinese beverage and ice cream franchise, in post-pandemic Indonesia. Employing Twitter sentiment analysis through the lens of the 7P marketing mix framework (Product, Price, Promotion, Place, People, Process, Physical Evidence), the research evaluates consumer attitudes towards Mixue amidst its explosive expansion and recent Halal certification. Data was collected through Twitter API and Scrapy, encompassing 7087 Indonesian-language tweets related to Mixue between January and February 2023. Sentiment analysis revealed an overall positive reception towards Mixue, particularly regarding its products and affordability. However, concerns emerged surrounding limited flavor variety, perceived high prices, promotional saturation, and queuing difficulties. Implications of these findings highlight the importance of franchise businesses adapting to evolving consumer preferences in the post-pandemic market. Mixue could benefit from diversifying its menu, addressing queuing issues, and tailoring promotions to enhance the customer experience. Gaining insights into public sentiment allows Mixue and other franchises to refine their strategies and maintain long-term success in the ever-changing Indonesian market. This research serves as a valuable stepping stone for further exploration into consumer preferences for specific flavor variations, conducting price comparisons, evaluating promotional effectiveness, and optimizing outlet placement and customer service.Addressing these aspects can fuel Mixue's continued growth and strengthen its position within the thriving Indonesian food and beverage landscape.

Ontology-Based Natural Language Processing for Sentimental Knowledge Analysis Using Deep Learning Architectures

When tested with popular datasets, sentiment categorization using deep learning (DL) algorithms will produce positive results. Building a corpus on novel themes to train machine learning methods in sentiment classification with high assurance, however, will be difficult. This study proposes a way for representing efficient features of a dataset into a word embedding layer of DL methods in sentiment classification known as KPRO (knowledge processing and representation based on ontology), a procedure to embed knowledge in the ontology of opinion datasets. This research proposes novel methods in ontology-based natural language processing utilizing feature extraction as well as classification by a DL technique. Here, input text has been taken as web ontology based text and is processed for word embedding. Then the feature mapping is carried out for this processed text using least square mapping in which the sentiment-based text has been mapped for feature extraction. The feature extraction is carried out using a Markov model based auto-feature encoder (MarMod_AuFeaEnCod). Extracted features are classified by utilizing hierarchical convolutional attention networks. Based on this classified output, the sentiment of the text obtained from web data has been analyzed. Results are carried out for Twitter and Facebook ontology-based sentimental analysis datasets in terms of accuracy, precision, recall, F-1 score, RMSE, and loss curve analysis. For the Twitter dataset, the proposed MarMod_AuFeaEnCod_HCAN attains an accuracy of 98%, precision of 95%, recall of 93%, F-1 score of 91%, RMSE of 88%, and loss curve of 70.2%. For Facebook, ontology web dataset analysis is also carried out with the same parameters in which the proposed MarMod_AuFeaEnCod_HCAN acquires accuracy of 96%, precision of 92%, recall of 94%, F-1 score of 91%, RMSE of 77%, and loss curve of 68.2%.

Aspect-Based Classification and Visualization of Twitter Sentiment Analysis Towards Online Food Delivery Services in Malaysia

Twitter has become a popular platform for the citizens of Malaysia. Twitter’s ease of expressing opinions could be used to evaluate and review Malaysian Online Food Delivery (OFD) providers. Due to competition from other OFDs in Malaysia, companies need to know customer feedback. OFD reviews are unstructured and massive, making comparisons difficult. Next, some websites evaluate OFD yet only consider pricing, delivery time, and customer experience. Customers cannot visualize the comparison based on users’ preferences, bilingual reviews, and less time-consuming to visualize OFD using the website. Thus, this study aims to design a web application system that uses Naïve Bayes to categorize Twitter sentiment analysis (SA) on Malaysia’s best OFD. It is based on customer satisfaction, visualizing the results, developing the system, and evaluating its accuracy, functioning and usability. Users can read about specific OFD by viewing Twitter SA visualization or comparing them directly. Five aspect-based SA types were presented: affordable price, promotion and discount, review rating, delivery time and condition of food delivered. Functionality testing demonstrated the accomplishment of all objectives. The training and testing data could predict OFD’s Twitter sentiment with 71.67% and 76.29% accuracy for English and Bahasa Melayu, respectively. The system’s usability produced a 94.64% average score using System Usability Scale and was considered “excellent”. Thus, it can be concluded that this study can solve the mentioned issues of OFD and ease the aspect-based comparison.

Twitter Sentiment Analysis with Gender Classification

Twitter Sentiment Analysis with Gender Classification

Multi-level deep Q-networks for Bitcoin trading strategies

The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology.

A multi-aspect framework for explainable sentiment analysis

The demand for explainable sentiment analysis has intensified, emphasizing the need for models that are both accurate and interpretable. This research introduces the Multi-Aspect Framework for Explainable Sentiment Analysis (MAFESA), a groundbreaking model that seamlessly integrates aspect extraction, sentiment prediction, and explainability. By harnessing the power of Latent Dirichlet Allocation (LDA) for aspect extraction and leveraging hierarchical neural networks for sentiment prediction, MAFESA achieves remarkable performance metrics. Notably, our framework outperforms state-of-the-art baseline models across benchmark datasets such as IMDB Movie Reviews, Amazon Product Reviews, and Twitter Sentiment Analysis. The inclusion of an explainability module, built around techniques like LIME, offers unparalleled insights into the model’s decision-making, ensuring predictions are transparent and trustworthy. Our performance evaluations, underscored by a thorough ablation study, cross-validation, and rigorous statistical tests, attest to MAFESA’s robustness, generalizability, and superiority. A detailed qualitative analysis further showcases the model’s adeptness at discerning aspect-level nuances and delivering clear explanations for sentiment predictions. This research not only sets a new benchmark in explainable sentiment analysis but also provides a holistic framework that balances prediction precision with interpretability.

Harmonizing Macro-Financial Factors and Twitter Sentiment Analysis in Forecasting Stock Market Trends

The surge in generative artificial intelligence technologies, exemplified by systems such as ChatGPT, has sparked widespread interest and discourse prominently observed on social media platforms like Twitter. This paper delves into the inquiry of whether sentiment expressed in tweets discussing advancements in AI can forecast day-to-day fluctuations in stock prices of associated companies. Our investigation involves the analysis of tweets containing hashtags related to ChatGPT within the timeframe of December 2022 to March 2023. Leveraging natural language processing techniques, we extract features, including positive/negative sentiment scores, from the collected tweets. A range of classifier machine learning models, encompassing gradient boosting, decision trees and random forests, are employed to train on tweet sentiments and associated features for the prediction of stock price movements among key companies, such as Microsoft and OpenAI. These models undergo training and testing phases utilizing an empirical dataset gathered during the stipulated timeframe. Our preliminary findings reveal intriguing indications suggesting a plausible correlation between public sentiment reflected in Twitter discussions surrounding ChatGPT and generative AI and the subsequent impact on market valuation and trading activities concerning pertinent companies, gauged through stock prices. This study aims to forecast bullish or bearish trends in the stock market by leveraging sentiment analysis derived from an extensive dataset comprising 500,000 tweets. In conjunction with this sentiment analysis derived from Twitter, we incorporate control variables encompassing macroeconomic indicators, Twitter uncertainty index and stock market data for several prominent companies.

Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression Models

The Analysis of Sentiments expressed on Twitter is a widely practiced application of Natural Language Processing (NLP) and Artificial Intelligence (AI). This process involves examining tweets to determine the emotional tone conveyed within the message. AI-based approaches are employed in Twitter sentiment analysis, typically following these steps: Data Collection, Data Preprocessing, and Sentiment Analysis, where AI techniques like Support Vector Machines (SVM) and Logistic Regression are utilized to categorize tweets into positive, negative, or neutral sentiments. Twitter data is a valuable source of information, serving diverse purposes such as real-time updates, user feedback, brand monitoring, market research, digital marketing, and political analysis. The Twitter API (Application Programming Interface) provides developers with tools and functionalities to access and interact with Twitter data, including tweets, user profiles, and timelines, enabling a wide range of applications and services. However, Twitter sentiment analysis presents challenges such as handling sarcasm, irony, colloquial language, and coping with the sheer volume and rapid flow of Twitter data. Nevertheless, with effective preprocessing techniques and AI methods, Twitter sentiment analysis can yield valuable insights into public opinion on various topics.

Nudging Customers towards Musicophile: Twitter Sentiment Analysis

Nudging Customers towards Musicophile: Twitter Sentiment Analysis

Twitter sentiment analysis using conditional generative adversarial network

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %.

Harnessing Twitter: Sentiment Analysis for Predicting Election Outcomes in India

This paper explores the potential of Twitter, a popular social media platform, as a tool for predicting election outcomes. Sentiment analysis has emerged as a powerful tool for predicting election outcomes, with numerous studies showcasing its effectiveness in various countries. For instance, research has utilized sentiment analysis to forecast election results in nations like the USA, India, Pakistan and other countries, demonstrating the utility of social media data in gauging public opinion and predicting electoral results [1]. Elections in India are always considered important events that most people look forward to the rapid growth of social media in the past has provided end users with powerful tools to share their ideas. Twitter, which is one such platform, provides daily updates on political events through various hashtags and trends. People react to political events and give their opinions. Our approach is to collect tweets from top political parties contesting the Gujarat Assembly Elections 2022, and then calculate sentiment scores. The database includes a variety of recent and well-liked tweets about a specific political party. Party tweets are generated with specific keywords like “BJP”, “AAP”,“Congress” and so on. In the context of India, Twitter sentiment analysis tools and classification have been used to predict the outcomes of state assembly elections, underscoring the potential of social media data in forecasting electoral results within the country [2]. We used standard machine learning algorithms like VADER sentiment analyzer on Random Forest and Decision Tree for our classification and testing data to classify tweets as positive and negative. As a result, this work uses sentiment analysis to evaluate tweets gathered from Twitter and forecast election outcomes. This work shows the growing influence of social media on politics and the feasibility of using such platforms for predictive analysis. The findings of this study could provide valuable insights for political parties, policymakers, and researchers interested in the intersection of social media and politics. Random Forest and Decision Tree models performed well in predicting election outcomes based on sentiment analysis on Twitter data with 89% and 86% respectively.

Sentiment Analysis: A Hybrid Approach on Twitter Data

In the present scenario, Internet communities, forum and blogging sites play a very crucial role to present opinions, views and the comments on various events. As the reachability of sites are beyond the control of national boundaries, sometimes this leads to the conviction and persuasion of thoughts without considering any legal subsequences, and also influence the belief of others as well, thus finding or identifying the sentiments of public from the social media content is one of the major research issue. Analysis of the sentiments of social media data is very difficult to understand as this typically does not exhibit a suspicious pattern in the flow of information like individual's actual opinion on a specific occasion etc. With the advancement of methodology and easy to use, the users of social media sites are now expressing their opinions, sharing their views and experiences through images, text, animation, audio, video etc. Because of the complexity the conventional text based sentiment analysis procedure, a lot of methods have been evolved, however studies suggest mostly more complicated procedure and approaches. Twitter® (or X), is one of the most common platform used widely by individual to express their opinions and sentiments on various events. Twitter sentiment analysis basically deals with the analysis of twitter quotes to find the hidden pattern in the sentiments expressed by the users in past. This paper aims to takes the challenges regarding social media sentiments analysis and developed a hybrid approach (Text and visual sentiment) on twitter data for sentiment analysis by using NLP-based opinion clustering, textual mining, emotion API and some machine learning techniques for visual ontology. Simulation result shows the significance of work.

Interpreting U.S. Public Opinion on Cross-Strait Relations during President Tsai Ing-wen’s First Term in Office: A Twitter Sentiment Analysis

Interpreting U.S. Public Opinion on Cross-Strait Relations during President Tsai Ing-wen’s First Term in Office: A Twitter Sentiment Analysis

Bitcoin volatility in bull vs. bear market-insights from analyzing on-chain metrics and Twitter posts.

Cryptocurrencies have emerged as a popular investment vehicle, prompting numerous efforts to predict market trends and identify metrics that signal periods of volatility. One promising approach involves leveraging on-chain data, which is unique to cryptocurrencies. On-chain data, extracted directly from the blockchain, provides valuable information, such as the hash rate, total transactions, or the total number of addresses that hold a specified amount of cryptocurrency. Some studies have also explored the relationship between social media sentiment and Bitcoin, using data from platforms such as Twitter and Google Trends. However, the quality of Twitter sentiment analysis has been lackluster due to suboptimal extraction techniques. This research proposes a novel approach that combines a superior sentiment analysis technique with various on-chain metrics to improve predictions using a deep learning architecture based on long-short term memory (LSTM). The proposed model predicts outcomes for multiple time horizons, ranging from one day to 14 days, and outperforms the Martingale (random walk) approach by over 9%, as measured by the mean absolute percentage error metric, as well as recent results reported in literature. To the best of our knowledge, this study may be among the first to employ this combination of techniques to improve cryptocurrency market prediction.

Twitter Sentiment Analysis Classification to Assess Public Opinion on Football Matches Using the Naïve Bayes Method

The Kanjuruhan tragedy has attracted many comments on various social media platforms. This research will compare the number of positive and negative comments on Twitter and social media and determine the accuracy of the classification method used. The data used in this study consisted of 2052 pieces, consisting of 1015 positive and 1037 negative pieces. To determine the effect of the amount of training data on the resulting accuracy, testing will be carried out three times with different combinations of training data and test data, namely 70:30, 80:20, and 90:10. The results of this study obtained the highest accuracy value of 79.6%. This program can be developed for other social media platforms such as Facebook, Instagram, and others

Classification and visualization: Twitter sentiment analysis of Malaysia’s private hospitals

Malaysia has many private’s hospitals. Thus, feedback is important to improve service quality, becoming reviews for other patients. Reviews use the channel service provided on social media, such as Twitter. Nevertheless, online reviews are unstructured and enormous in volume, which leads to difficulties in comparing private hospitals. In addition, no single websites compare private hospitals based on users’ interests, bilingual reviews, and less time-consuming. Due to that, this study aims to classify and visualize the Twitter sentiment analysis of private hospitals in Malaysia. The scope focuses on five factors: 1) administrative procedure, 2) cost, 3) communication, 4) expertise, and 5) service. Term frequency-inverse document frequency is used for text mining, information retrieval techniques, and the Naïve Bayes, a machine learning algorithm for the classification. The user can visualize the specified state’s private hospitals and compare them with any selected state. The system’s functionality and usability have been tested to ensure it meets the objectives. Functionality testing proved that the private hospital’s Twitter sentiment could be predicted based on the training and testing data as intended, with 77.13% and 77.96% accuracy for English and Bahasa Melayu, respectively, while the system usability scale based on the usability testing resulted in an average final score of 95.42%.

A Twitter sentiment analysis dashboard for Covid-19: The case of Kansas

This study focuses on analyzing people's tweets in the state of Kansas during the Covid-19 pandemic. Data was collected using the Twitter API from January to September of 2020. We developed a custom back-end algorithm to collect and accumulate daily tweets over time. We then cleaned the tweets and performed sentiment analysis to identify positive, negative, and neutral tweets. Finally, we developed a front-end algorithm to generate a custom dashboard containing several interactive visualizations and made the dashboard available online. The study describes the background, methodology and results.

Twitter sentiment analysis: An estimation of the trends in tourism after the outbreak of the Covid-19 pandemic

Abstract The novel coronavirus pandemic drastically impacted economies in 2020, including travel and tourism. The nationwide lockdown to curb infection has led people to use social media such as Twitter to express their opinions and share information on several issues. This paper focuses on sentiment analysis using Indian tourist Tweets during Covid-19 using Python and the maximum likelihood method to determine the parameters. The sentiment analysis yielded valuable insights into which sites will revive quickly; the analysis of the Tweets using sentiment analysis will help in predicting the revival of the tourism sector after the pandemic. The various algorithm scores will help in predicting the best ways to enhance the customer experience at various sites in the tourism sector. Further, the study will enable the tourism sector to design a revival strategy in the Covid-19 pandemic.

Learning to Detect Human Emotions in Digital World by Integrating Ensemble Voting Classifiers

Due to the expansion of world of the internet and the quick acceptance of platforms for social media, information is now able to exchange in ways never previously imagined in history of mankind. A social networking site like Twitter offers a forum where people may interact, discuss, as well as respond to specific issues via short entries, like tweets of 140 characters and fewer. Users may engage by utilizing the comment, like and share tabs on texts, videos, images and other content. Although platforms for social media are now so extensively utilized, individuals are creating as well as sharing so much information than shared before, which can be incorrect or unconnected to reality. It is difficult to identify erroneous or inaccurate statements in textual content autonomously and find emotions of people. In this paper, we suggest an Ensemble method for sentiment and emotion analysis. Different textual features of actual and Emotion and sentiment have been utilized. We used a publicly accessible dataset of twitter sentiment analysis that included total 48,247 authenticated tweets out of 23,947 of which were authentic positive texts labeled as binary 0s and 24,300 of which were negative texts labeled as binary 1s. In order to assess our approach, we used well-known (ML) machine learning techniques, these are Logistic Regression (LR), AdaBoost, Decision Tree (DT), SGD, XG-Boost as well as Naive Bayes. In order to get more accurate findings, we created a multi-model sentiment and emotion analyzing system utilizing the ensemble approach and the classifiers stated above. Our recommended ensemble learner method outperforms individual learners, according to an experimental study.

Comparative study of machine learning algorithms for Kannada twitter sentimental analysis

Comparative study of machine learning algorithms for Kannada twitter sentimental analysis