Social Media Sentiment Research Articles

Trucking in the Era of COVID-19

COVID-19 resulted in health and logistical challenges for many sectors of the American economy, including the trucking industry. This study examined how the pandemic impacted the trucking industry, focused on the pandemic’s impacts on company operations, health, and stress of trucking industry employees. Data were collected from three sources: surveys, focus groups, and social media posts. Individuals at multiple organizational levels of trucking companies (i.e., supervisors, upper-level management, and drivers) completed an online survey and participated in online focus groups. Data from focus groups were coded using a thematic analysis approach. Publicly available social media posts from Twitter were analyzed using a sentiment analysis framework to assess changes in public sentiment about the trucking industry pre- and during-COVID-19. Two themes emerged from the focus groups: (1) trucking company business strategies and adaptations and (2) truck driver experiences and workplace safety. Participants reported supply chain disruptions and new consumer buying trends as having larger industry-wide impacts. Company adaptability emerged due to freight variability, leading organizations to pivot business models and create solutions to reduce operational costs. Companies responded to COVID-19 by accommodating employees’ concerns and implementing safety measures. Truck drivers noted an increase in positive public perception of truck drivers, but job quality factors worsened due to closed amenities and decreased social interaction. Social media sentiment analysis also illustrated an increase in positive public sentiment towards the trucking industry during COVID-19. The pandemic resulted in multi-level economic, health, and social impacts on the trucking industry, which included economic impacts on companies and economic, social and health impacts on employees within the industry levels. Further research can expand on this study to provide an understanding of the long-term impacts of the pandemic on the trucking industry companies within the industry and segments of the trucking industry workforce.

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data analytics for analyzing stock exchange markets. This is due to the fact that the sentiments around major global events like Brexit or COVID-19 significantly affect business decisions and investor perceptions, as well as transactional trading statistics and index values. Hence, in this research, we examined a case study from the Brexit event to assess the influence that feelings on the subject have had on the stock markets of European Union (EU) nations. Brexit has implications for Britain and other countries under the umbrella of the European Union (EU). However, a common point of debate is the EU’s contribution preferences and benefit imbalance. For this reason, the Brexit event and its impact on stock markets for major contributors and countries with minimum donations need to be evaluated accurately. As a result, to achieve accurate analysis of the stock exchanges of different EU nations from two different viewpoints, i.e., the major contributors and countries contributing least, in response to the Brexit event, we suggest an optimal deep learning and machine learning model that incorporates social media sentiment analysis regarding Brexit to perform stock market prediction. More precisely, the machine learning-based models include support vector machines (SVM) and linear regression (LR), while convolutional neural networks (CNNs) are used as a deep learning model. In addition, this method incorporates around 1.82 million tweets regarding the major contributors and countries contributing least to the EU budget. The findings show that sentiment analysis of Brexit events using a deep learning model delivers better results in comparison with machine learning models, in terms of root mean square values (RMSE). The outcomes of stock exchange analysis for the least contributing nations in relation to the Brexit event can aid them in making stock market judgments that will eventually benefit their country and improve their poor economies. Likewise, the results of stock exchange analysis for major contributing nations can assist in lowering the possibility of loss in relation to investments, as well as helping them to make effective decisions.

Does it really pay off for investors to consider information from social media?

This paper raises the question whether investors can learn something from social media sentiment that they do not already know from (existing) financial information disclosed by companies and financial analysts. Therefore, the relationship between financial information and Refinitiv’s MarketPsych social media sentiment index is explored. The paper introduces adjusted social media sentiment, which corrects social media sentiment for the impact of financial information such as earnings surprises, analyst forecast revisions, new dividends, and 8-K filings. It turns out that adjusted social media sentiment is related to subsequent short-term stock returns. This is particularly true for stocks with negative (adjusted) sentiment. Moreover, looking at long-term holding returns the paper does not find compelling evidence for reversals suggesting that (adjusted) social media sentiment reflects information about the prospects of the firm.

Gifted Education on Reddit: A Social Media Sentiment Analysis

Social media continues to be an ever-present part of people’s lives. One of the largest social media websites, Reddit has more than 430 million unique visitors monthly. What is unknown to scholars is how gifted education fits into this modern form of communication. In our research, we examined how gifted education is discussed over Reddit using text mining in combination with sentiment analysis. In addition, our research conditioned sentiment on variables such as self-disclosure, interest, and controversy. We found that, overall, discussion of giftedness was neutral in terms of sentiment and used common language across subreddits. We also found that the language used and distribution of sentiment were similar between self-disclosures and non-self-disclosures.

Social Media Sentiment Analysis Based on Dependency Graph and Co-occurrence Graph

Social Media Sentiment Analysis Based on Dependency Graph and Co-occurrence Graph

Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis

Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis

Sentiment Polarity Identification of Social Media content using Artificial Neural Networks

Sentiment of people about consumer goods and government policies for decision making is normally collected through feedback forms, surveys etc. The social network sites and micro blogging sites are considered a very good source of information nowadays because people share and discuss their opinions about a certain topic freely. With the increased use of technology and social media, people proactively express their opinion through social media sites like Twitter, Facebook, Instagram etc. A social media sentiment analysis can help companies to understand how people feel about their products. On the other hand, extracting the sentiment from social media text is a challenging task due to the complexity of natural language processing of social media language. Often these messages reflect the emotion, opinion and sentiment of the public through a mix of text, image, emoticons etc. These statements are often called electronic Word of Mouth (eWOM) and are much prevalent in business and service industry to enable customers to share their point of view.

Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media

World Wide Web enables its users to connect among themselves through social networks, forums, review sites, and blogs and these interactions produce huge volumes of data in various forms such as emotions, sentiments, views, etc. Sentiment Analysis (SA) is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive, negative, and neutral. However, Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing (NLP). Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of applications. So, there is a need exists to develop a proper technique for both identification and classification of sentiments in social media. To get rid of these problems, Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning capability. The current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification (SOADL-SAC) for social media. The presented SOADL-SAC model involves the proper identification and classification of sentiments in social media. In order to attain this, SOADL-SAC model carries out data preprocessing to clean the input data. In addition, Glove technique is applied to generate the feature vectors. Moreover, Self-Head Multi-Attention based Gated Recurrent Unit (SHMA-GRU) model is exploited to recognize and classify the sentiments. Finally, Seeker Optimization Algorithm (SOA) is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier results. In order to validate the enhanced outcomes of the proposed SOADL-SAC model, various experiments were conducted on benchmark datasets. The experimental results inferred the better performance of SOADL-SAC model over recent state-of-the-art approaches.

The Causal Relationship between Social Media Sentiment and Stock Return: Experimental Evidence from an Online Message Forum

The Causal Relationship between Social Media Sentiment and Stock Return: Experimental Evidence from an Online Message Forum

The Causal Relationship between Social Media Sentiment and Stock Return: Experimental Evidence from an Online Message Forum

The Causal Relationship between Social Media Sentiment and Stock Return: Experimental Evidence from an Online Message Forum

Students’ Perceptions of E-Learning in Malaysian Universities: Sentiment Analysis Based Machine Learning Approach

Aim/Purpose: To gain insight into the opinions and reviews of Malaysian university students regarding e-learning systems, thereby improving the quality and services of these systems and resolving any problems, concerns, and issues that may exist within the institution. Background: This exploratory study examines the students’ perceptions of e-learning in Malaysia based on Sentiment Analysis (SA) to gain a clear insight into their feelings about the quality of e-learning systems and related services in Malaysian universities to determine whether these opinions are positive or negative. Methodology: The data was collected from Twitter; the Full Archive Search API Premium v1.1 tire was chosen to access the tweets from November 1, 2019, to December 30, 2020. The R programming language library package “rtweet” was applied to access the search API and query the tweets. To classify students’ opinions, sentiment analysis-based Machine Learning (ML) with Support Vector Machine (SVM) was utilized. Rapid Miner, a statistical and data mining tool, was used to determine the sentiment of tweets and the accuracy of the ML algorithm. After preparing the data, RapidMiner was used to pre-process and classify the final 1201 tweets based on sentiment, and National Research Council (NRC) word-emotion lexicon was used to detect the presence of eight emotions in the tweets. The confusion matrix is used to determine the classifier’s performance. Contribution: This research provided evidence for the effective use of sentiment analysis as an indicator that may contribute to the development of educational systems, specifically, e-learning systems in Malaysian universities. Findings: Based on the findings, the majority of students have a positive opinion about e-learning systems in Malaysian universities. Precisely, the results showed that 65% of sentiments were classified as positive and 35% as negative. Moreover, among the eight emotions, the majority of the tweets expressed a higher level of trust, anticipation, and joy. Recommendations for Practitioners: The study findings could help classify the teachers’ strengths and weaknesses graphically based on the students’ positive and negative feedback. These findings would also help decision-makers and educationalists be more aware of students’ feelings (sentiments) and concerns. Thus, using social media sentiment analysis should be encouraged as a valuable source of information that may assist their educational decision-making, e-learning development, and performance evaluation. Recommendation for Researchers: The findings may encourage other researchers to apply SA based ML approach and use Twitter as a data source to discover users’ opinions about certain issues in learning and teaching processes. Impact on Society: Our study confirmed that social media data could provide valuable and supportive information about educational systems and procedures in e-learning for appropriate decision-making regarding future development and strategies. Future Research: Future work can experiment with other classification models and different ML classification algorithms as well as other feature extraction methods and compare the results to find the best accuracy that can improve the classification results

Social Media Multimodal Information Analysis based on the BiLSTM-Attention-CNN-XGBoost Ensemble Neural Network

Social media users internalise information in a multimodal context. Social media functions as a primary information source for disaster situational awareness encompassing texts, photographs, videos, and other multimodal information widely used in emergency management. Applying ensemble learning to social media sentiment analysis has garnered much scholarly attention, albeit with limited research on rescue and its sub-domain, which is characterised as a major complexity. A multimodal information categorisation model based on hierarchical feature extraction was proposed in this study. The information of multiple modes is first mapped to a unified text vector space in modelling the semantic content at the sentence and multimodal information levels in the multimodal information. Multiple deep learning (DL) models were subsequently applied to model the semantic content at the aforementioned levels. This study offers a BiLSTM-Attention-CNN-XGBOOST ensemble neural network model to acquire extensive multimodal information characteristics. Based on the empirical outcomes, this method precisely extracted multimodal information features with an accuracy exceeding 85% and 95% for Chinese- and English-language datasets, respectively.

Approximating Viewership of Streaming T.V Programs Using Social Media Sentiment Analysis

Approximating viewership of a program is essential for broadcast networks and cable (TNT, AMC, and HBO) as they profit by selling advertising space during programming (airing of the show). With the advent and increased popularity of streaming services such as Netflix and Amazon Prime Video, the model for providing television content to viewers has fundamentally changed. However, this change in the delivery model for television programs has also made it more challenging to determine the actual viewership of television programs on streaming services. Because television programs are no longer aired in pre-determined timeslots, viewership shares cannot be determined. To make matters even more complicated, streaming services like Netflix and Amazon Prime Video are proprietary platforms. They have traditionally kept viewership numbers closely guarded and have not released them to the public. This paper proposes a methodology to approximate the viewership of streaming television shows. This is achieved by exploiting data from social networks, i.e., publicly available information and sentiment score derived from sentiment analysis on tweets published about television programs, using Random Forest classifier. The proposed methodology achieved 85% accuracy in predicting the viewership of the streaming shows.

Design change prediction based on social media sentiment analysis

AbstractThe use of artificial intelligence (AI) techniques to uncover customer sentiment is not uncommon. However, the integration of sentiment analysis with research in design change prediction remains an untapped potential. This paper presents a method that uses social media sentiment analysis to identify opportunities for design change and the set of product components affected by the change. The method builds on natural language processing to determine change candidates from textual data and uses dependency modeling to reveal direct and indirect change propagation paths arising from the change candidates. The method was applied in a case example where 3665YouTubecomments on a diesel engine were analyzed. Based on the results, two engine components were recommended for design change with six others predicted as likely to be affected through change propagation. The findings suggest that the method can be used to aid decision quality in product planning through a better understanding of the change impact associated with the opportunities identified.

Identifying social media sentiment trend: Analysis & Comparison of Covid-19 with Global Financial Crisis

Twitter Sentiment Analysis is an imminently useful field that addresses the idea of investigating elements and messages posted on the social media site Twitter in terms of expressed sentiments. This research aims to provide analysts with a comprehensive methodology in which data gathered from Twitter can be rapidly analyzed to determine the emotions and sentiments. The created process will allow for the trend of collective sentiments to be analyzed at any given time. This analysis is carried out in both a Pre-Covid and Post-Covid scenario, and the sentiments are compared to a post-2008 financial crisis scenario to examine the interrelationship between general sentiment trends and the state of financial markets.

Social Media Sentiments and Firm Value

ABSTRACT This paper examines the link between social media sentiments and firm value. Using a sample of Fortune 500 firms from 2010 to 2017, we find that positive social media sentiments increase firm value, whereas negative social media sentiments do not decrease firm value. Next, we consider the link between social media sentiments and firm value in the context of the firms’ environmental, social, and governance performance. Our main findings remain unchanged for firms with low environmental, social, and governance performance. We also find that these results are more pronounced for firms in industries with high advertising spending. Our results are supported by a series of robustness tests.

Exploring Consumer Emotions in Pre-Pandemic and Pandemic Times. A Sentiment Analysis of Perceptions in the Fine-Dining Restaurant Industry in Bucharest, Romania.

This research paper aims to analyse how consumer emotions have evolved during the pandemic period in comparison with the pre-pandemic period in relation to restaurant demand in the Romanian fine-dining industry and uses valuable information based on social-media sentiment analysis and content analysis. Focusing on theories of consumer behaviour, the study aims to emphasize how, under the influence of an epidemic crisis caused by an infectious disease, individual behaviour adapts to the “new normal”, embracing a series of changes in the preferences, attitudes, and cognitive choice-making processes. The article takes into account a comparative analysis of the consumer emotions between the pre-COVID-19 pandemic period (2010–2019) and the pandemic period (2020–present), based on the online reviews provided by customers for five fine-dining restaurants from Bucharest, the capital city of Romania: The Artist, Relais & Chateaux Le Bistrot Francais, Casa di David, Kaiamo, and L’Atelier. The research was based on two mining analyses—content analysis and sentiment analysis—and explored the emotional intent of words, with the data being collected from TripAdvisor through web-scrapping. The empirical results defined the fine-dining experience during the pandemic as being associated with the quality of the dishes and also with the quality of the service. The overall consumer sentiment in the direction of the restaurants analyzed is positive. The sentiment research found that throughout the epidemic, the consumers’ attitudes about restaurants deteriorated. In this sense, consumers seem to be less satisfied with the restaurants’ services than before the pandemic. This is another thing that the restaurants had difficulties in when adapting their operations for the pandemic.

Cryptocurrency ecosystems and social media environments: An empirical analysis through Hawkes’ models and natural language processing

We analyse, using a mixture of statistical models and natural language process techniques, what happened in social media from June 2019 onwards to understand the relationships between Cryptocurrencies’ prices and social media, focusing on the rise of the Bitcoin and Ethereum prices. In particular, we identify and model the relationship between the cryptocurrencies market price changes, and sentiment and topic discussion occurrences on social media, using Hawkes’ Model. We find that some topics occurrences and rise of sentiment in social media precedes certain types of price movements. Specifically, discussions concerning governments, trading, and Ethereum cryptocurrency as an exchange currency appear to negatively affect Bitcoin and Ethereum prices. Those concerning investments, appear to explain price rises, whilst discussions related to new decentralized realities and technological applications explain price falls. Finally, we validate our model using a real case study: the already famous case of ”Wallstreetbet and GameStop”11https://www.economist.com/finance-and-economics/2021/02/06/how-wallstreetbets-works. that took place in January 2021.

Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation.

Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data. However, it is hard to predict stock movement based on a single classifier. In this study, we proposed a multichannel collaborative network by incorporating candlestick-chart and social-media data for stock trend predictions. We first extracted the social media sentiment features using the Natural Language Toolkit and sentiment analysis data from Twitter. We then transformed the stock’s historical time series data into a candlestick chart to elucidate patterns in the stock’s movement. Finally, we integrated the stock’s sentiment features and its candlestick chart to predict the stock price movement over 4-, 6-, 8-, and 10-day time periods. Our collaborative network consisted of two branches: the first branch contained a one-dimensional convolutional neural network (CNN) performing sentiment classification. The second branch included a two-dimensional (2D) CNN performing image classifications based on 2D candlestick chart data. We evaluated our model for five high-demand stocks (Apple, Tesla, IBM, Amazon, and Google) and determined that our collaborative network achieved promising results and compared favorably against single-network models using either sentiment data or candlestick charts alone. The proposed method obtained the most favorable performance with 75.38% accuracy for Apple stock. We also found that the stock price prediction achieved more favorable performance over longer periods of time compared with shorter periods of time.

Impact of social media sentiments in stock market predictions: A bibliometric analysis

The main objective of the paper is to anticipate a bibliometric analysis of the research on stock market prediction using social media sentiments. The study has taken out a total of 1450 documents from the year Jan 2010 to Dec 2020. This study attempts to identify a significant journal that has maximum documents, most prolific author, most cited papers, countries, institutions, co-authorship network analysis map, inter-country co-authorship network analysis map, and keyword occurrences. The study has used the Scopus database for analyzing the large set of data of research papers that are counted in the study. And the VOSviewer software is used for generating the maps such as co-authorship analysis network map and keyword occurrence network.