Twitter Sentiment Research Articles

Analysts’ stock ratings and the predictive value of news and Twitter sentiment

ABSTRACT This study investigates the relationship between the stock ratings of professional analysts and company-level sentiment. I find that investor sentiment expressed in tweets and news articles displays a positive relationship with professional analysts’ stock ratings and that the sentiment conveyed through tweets has a stronger effect than the sentiment from conventional news articles. Furthermore, the effect of sentiment derived from tweets and news articles on analysts’ ratings is stronger when the sentiment valence is the same (negative or positive) and weaker when the sentiment valence differs. The findings shed light on the link between investor sentiment and analysts’ stock ratings by demonstrating that analysts’ stock ratings are influenced by investor sentiment.

Twitter sentiment analysis of lockdown/pandemic in India by using deep learning : case study of COVID-19

Twitter sentiment analysis of lockdown/pandemic in India by using deep learning : case study of COVID-19

Comparative Analysis and Design of Different Approaches for Twitter Sentiment Analysis and classification using SVM

Companies and organizations have always found that the views and feedback of the community are their most important and valuable resource. With everyone using social media more and more, it makes it possible to analyze and evaluate things in ways that have never been done before. Before, organizations had to use methods that were unusual, time-consuming, and prone to mistakes. This way of analyzing fits right into the field of "sentiment analysis." Sentiment analysis is a broad field that deals with putting user-generated text into well-defined groups. There are a number of tools and algorithms that can be used to detect and analyze sentiment. For example, supervised machine learning algorithms can be trained with training data and then used to classify the target corpus. Lexical techniques, which use a dictionary-based annotated corpus to do classification, and hybrid tools, which are a mix of machine learning and lexicon-based algorithms, are also used. In this paper, we used Weka's Support Vector Machine (SVM) to analyze how people feel about something. SVM is a popular supervised machine learning algorithm used to find the polarity of text. The main objective is to analyze the emotions expressed in tweets using various simulations of artificial intelligence that classify tweets as positive or negative. If a tweet has both positive and negative components, the more prevalent component should be chosen as the closing statement. Emojis, usernames, and hashtags in tweets should be controlled and transformed into a standard development. Sincerely, these events' planners have started looking into these inconspicuous web blogs (online diaries) to acquire a feel for their niche. On other discreet sites, they routinely monitor and respond to customer feedback. Better means of seeing and combining a broad assessment are one challenge. A few people, including Facebook, Twitter, and Instagram, were really introduced to social affiliation stages a year ago. The vast majority of individuals utilize internet entertainment to express their thoughts about objects, places, or people. Systems Twitter, a less common platform for publishing material to blogs, is a huge repository of well-known reviews for various persons, services, associations, and products, among other things. Assessment examinations are reviews of the public assessment structures. What is said on Twitter has a substantial context thanks to a mixture of opinions. The widespread accessibility of online tests and virtual entertainment posts in the media provides connection with crucial examination to undermine expert judgments and direct their boosting strategies to relaxing and client conclusions. In this way, virtual distraction anticipates playing a significant role in influencing the general exposure of the companies or objects selected. This study highlights the many approaches used for item depiction analyses. Check topics on Twitter to see if the general public is acting in a favorable, negative, or neutral manner.

Twitter Sentiment Analysis and Its Effect on Society on the Fall of Cryptocurrency


 
 
 Many employees still have difficulty entering data and searching for appropriate data. Employees still input data manually, and it takes a long time. The existence of a soil observation application in the field of PT. Anugrah Sumber Makmur Minamas Research Center was created with the aim of assisting employees in entering data and searching for data quickly and accurately. This application is made using the PHP programming language and MySQL as the database. The system development method in making this application uses the design sprint method. Based on tests conducted on users, this application functionally runs as expected, and the success rate is 92%
 
 

Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble.

The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries.

Quantifying the relationship between public sentiment and urban environment in Barcelona

Public sentiment provides an important social reference for urban management and planning. The relationship between public sentiment and a single type of land use has yielded stable results in previous studies. Hitherto, there has been relatively little research on the correlation of the entire urban environment with public sentiment. Based on the unit of statistical area in Barcelona city, this research uses Twitter sentiment to represent public sentiment and develops a regression model for understanding the interrelationship of four layers: sociodemographic, built-environment, human mobility and socioeconomic activities. The result shows that: 1) The long-term spatial difference in public sentiment has correlations with the urban environment, though it is not decisive. 2) Regardless of disruptive events that are directly associated with public sentiments, the wealthier areas show a more positive correlation with higher public sentiment. 3) The distribution of sentiment tweets (non-neutral) has a close relationship with places where there is a high flow of human activities. This study contributes to the systematic literature of urban applications of sentiment analysis with new empirical observations and a transferable methodology.

A sentiment analysis of the Black Lives Matter movement using Twitter

As more attention is brought to the issue of racial injustice, public sentiments and opinions on racial issues are increasingly important to track. At the same time, recent progress in machine learning and natural language processing methods, coupled with the growing amount of available data for training and analysis, allows researchers to extract sentiments from text data at large scales. We applied a natural language processing framework to study public sentiment surrounding the Black Lives Matter (BLM) movement. Specifically, we used a state-of-the-art BERT model fine-tuned for Twitter sentiment classification to predict the sentiment from approximately 1 million tweets from July 2013 to March 2021 related to BLM. The BERT model was trained on the Sentiment 140 dataset on which it obtained an AUC of 0.97 on the training data and 0.94 on testing data, outperforming other machine learning models. We found that retweet frequency and word count frequency were able to illustrate important themes in the BLM movement as well as indicate events of significant importance to the movement. Additionally, sentiment analysis revealed which of these themes and events were associated with positive public sentiment, such as social justice, and which were associated with negative sentiment, such as police brutality. Our analyses can also be applied to better understand other social and political movements to aid related research and activism.

Brazilian stock market performance and investor sentiment on Twitter

PurposeThis study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.Design/methodology/approachThe study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.FindingsIn the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.Originality/valueThe study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.

A longitudinal study of the evolution of opinions about open access and its main features: a twitter sentiment analysis

A longitudinal study of the evolution of opinions about open access and its main features: a twitter sentiment analysis

Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis

Practical demands and academic challenges have both contributed to making sentiment analysis a thriving area of research. Given that a great deal of sentiment analysis work is performed on social media communications, where text frequently ignores the rules of grammar and spelling, pre-processing techniques are required to clean the data. Pre-processing is also required to normalise the text before undertaking the analysis, as social media is inundated with abbreviations, emoticons, emojis, truncated sentences, and slang. While pre-processing has been widely discussed in the literature, and it is considered indispensable, recommendations for best practice have not been conclusive. Thus, we have reviewed the available research on the subject and evaluated various combinations of pre-processing components quantitatively. We have focused on the case of Twitter sentiment analysis, as Twitter has proved to be an important source of publicly accessible data. We have also assessed the effectiveness of different combinations of pre-processing components for the overall accuracy of a couple of off-the-shelf tools and one algorithm implemented by us. Our results confirm that the order of the pre-processing components matters and significantly improves the performance of naïve Bayes classifiers. We also confirm that lemmatisation is useful for enhancing the performance of an index, but it does not notably improve the quality of sentiment analysis.

One year of European premiers leadership and empathy in times of global pandemic: a Twitter sentiment analysis

One year of European premiers leadership and empathy in times of global pandemic: a Twitter sentiment analysis

Topic Sentiment Analysis for Twitter Data in Indian Languages Using Composite Kernel SVM and Deep Learning

Sentiment analysis of public opinions on social networks, such as Twitter or Facebook, can provide us with valuable information, which has a wide range of applications. But the efficiency and accuracy of the automated methods for Twitter sentiment analysis are hindered by the special characteristics of the Twitter data. The Twitter data is generally noisy, high-dimensional, and it has complex syntactic and semantic structures. Sentiment analysis of Twitter data in Indian languages is more challenging because the data is multilingual and code-mixed. In this article, we propose various composite kernel functions, each of which is used with Support Vector Machines (SVM) for developing a model for topic sentiment analysis of Twitter data in Indian languages. Each composite kernel function is constructed by taking the weighted summation of multiple single kernel functions defined by us. In addition to our proposed composite kernel SVM method, we use several state-of-the-art deep learning classifiers for topic sentiment classification. Since any suitable Twitter dataset in Indian languages is not available for conducting our experiments, we have developed our own datasets by collecting tweets related to five different Twitter trending topics in India. To prove the robustness and generalization capability of the proposed models, they are also evaluated on the US airline Twitter dataset which is a publicly available benchmark English dataset. The empirical study exhibits that the proposed composite kernel SVM method is effective for the sentiment classification task. In the case of Indian language datasets, the proposed composite kernel SVM method achieves the highest average accuracy of 74% and the highest average F-score of 0.73. On the other hand, the deep learning-based method achieves the average accuracy and the average F-score of 71.31% and 0.70, respectively. In the case of the US airline Twitter dataset, the proposed composite kernel SVM method achieves the average accuracy of 83% and the average F-score of 0.82, which are higher than that of the deep learning-based method.

A cross-country analysis of macroeconomic responses to COVID-19 pandemic using Twitter sentiments.

The COVID-19 pandemic has had a devastating impact on the global economy. In this paper, we use the Phillips curve to compare and analyze the macroeconomics of three different countries with distinct income levels, namely, lower-middle (Nigeria), upper-middle (South Africa), and high (Canada) income. We aim to (1) find macroeconomic changes in the three countries during the pandemic compared to pre-pandemic time, (2) compare the countries in terms of response to the COVID-19 economic crisis, and (3) compare their expected economic reaction to the COVID-19 pandemic in the near future. An advantage to our work is that we analyze macroeconomics on a monthly basis to capture the shocks and rapid changes caused by on and off rounds of lockdowns. We use the volume and social sentiments of the Twitter data to approximate the macroeconomic statistics. We apply four different machine learning algorithms to estimate the unemployment rate of South Africa and Nigeria on monthly basis. The results show that at the beginning of the pandemic the unemployment rate increased for all the three countries. However, Canada was able to control and reduce the unemployment rate during the COVID-19 pandemic. Nonetheless, in line with the Phillips curve short-run, the inflation rate of Canada increased to a level that has never occurred in more than fifteen years. Nigeria and South Africa have not been able to control the unemployment rate and did not return to the pre-COVID-19 level. Yet, the inflation rate has increased in both countries. The inflation rate is still comparable to the pre-COVID-19 level in South Africa, but based on the Phillips curve short-run, it will increase further, if the unemployment rate decreases. Unfortunately, Nigeria is experiencing a horrible stagflation and a wild increase in both unemployment and inflation rates. This shows how vulnerable lower-middle-income countries could be to lockdowns and economic restrictions. In the near future, the main concern for all the countries is the high inflation rate. This work can potentially lead to more targeted and publicly acceptable policies based on social media content.

Competitive Capsule Network Based Sentiment Analysis on Twitter COVID’19 Vaccines

COVID-19 is an extremely contagious virus that has rapidly spread around the world. This disease has infected people of all ages in India, from children to the elderly. Vaccination, on the other hand, is the only way to preserve human lives. In the midst of a pandemic, it’s critical to know what people think of COVID-19 immunizations. The primary goal of this article is to examine corona vaccination tweets from India’s Twitter social media. This study introduces CompCapNets, a unique deep learning approach for Twitter sentiment classification. The results suggest that the proposed method outperforms other strategies when compared to existing traditional methods.

Analysis of Ensemble Classification of Twitter Sentiments Using New Dependency Tree Based Approach

The vast amount of data available on social media and microblogs can be a valuable resource for mining opinions or for analyzing the overall mood of the public. This helps in identifying potential customers, exploring market trends and predicting events. Analyzing twitter data is comparatively difficult due to the large amount of irregularities present in tweets. Many approaches that use sentiment dictionaries and machine learning have been proposed until now. In this paper, we present a new feature that is extracted using dependency parsing and an emotion lexicon. This feature, along with n-grams, syntactic n-grams and lexicon-based features, is used to classify the tweets. We also use custom dictionaries to identify slang words, SMS short forms, emoticons and word contractions. The performance of various classification algorithms and ensemble techniques is compared. Our results show that the new feature along with the ensemble framework improves sentiment classification.

Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index

We examine the quantile connectedness of returns between the recently developed S&P 500 Twitter Sentiment Index and various asset classes. Rather than a mean-based connectedness measure, we apply quantile-connectedness to explore connectedness of means and, especially, extreme left and right tails of distributions. Using mean-based connectedness measures, the level of return connectedness between the twitter sentiment index and all financial markets is a modest 46%. However, when applying a novel quantile-based connectedness approach, we find that levels of tail-connectedness are much stronger, up to 82%, at extreme upper and lower tails. This suggests that the impact of sentiment on financial markets is much stronger during extreme positive/negative sentiment shocks. Moreover, return connectedness measures are less volatile during extreme events. Net connectedness analysis shows that the Twitter sentiment index acts as a net transmitter of return spillovers, highlighting the leading role of investor sentiment on predicting other financial markets.

Highlighting smart city mirages in public perceptions: A Twitter sentiment analysis of four African smart city projects

‘Smart cities’ strategies are often promoted as a panacea to existing urban planning issues such as local economic development, public safety, traffic congestion, and infrastructure maintenance. While these initiatives continue to be increasingly sought out and publicly promoted by governments across the globe, little is known about the influence of smart city branding campaigns on public perceptions of such projects. To explore this dynamic, we conducted a sentiment analysis of tweets regarding four smart city projects in Africa using the NRC Emotion Lexicon. The analysis revealed strong and consistent positive associations with words annotated with the emotions of Anticipation, Trust, and Joy. Based on these cross-case trends, we speculate that given their underwhelming outputs, the larger smart city agenda and its accompanying strategic positive framing have contrived a potentially pervasive and illusionary sentiment of “smart city mirages” among the public consciousness.

Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods.

Financial economic research has extensively documented the fact that the impact of the arrival of negative news on stock prices is more intense than that of the arrival of positive news. The authors of the present study followed an innovative approach based on the utilization of two artificial intelligence algorithms to test that asymmetric response effect. Methods: The first algorithm was used to web-scrape the social network Twitter to download the top tweets of the 24 largest market-capitalized publicly traded companies in the world during the last decade. A second algorithm was then used to analyze the contents of the tweets, converting that information into social sentiment indexes and building a time series for each considered company. After comparing the social sentiment indexes’ movements with the daily closing stock price of individual companies using transfer entropy, our estimations confirmed that the intensity of the impact of negative and positive news on the daily stock prices is statistically different, as well as that the intensity with which negative news affects stock prices is greater than that of positive news. The results support the idea of the asymmetric effect that negative sentiment has a greater effect than positive sentiment, and these results were confirmed with the EGARCH model.

Social Network Analysis of COVID-19 Sentiments: 10 Metropolitan Cities in Italy.

The pandemic spread rapidly across Italy, putting the region’s health system on the brink of collapse, and generating concern regarding the government’s capacity to respond to the needs of patients considering isolation measures. This study developed a sentiment analysis using millions of Twitter data during the first wave of the COVID-19 pandemic in 10 metropolitan cities in Italy’s (1) north: Milan, Venice, Turin, Bologna; (2) central: Florence, Rome; (3) south: Naples, Bari; and (4) islands: Palermo, Cagliari. Questions addressed are as follows: (1) How did tweet-related sentiments change over the course of the COVID-19 pandemic, and (2) How did sentiments change when lagged with policy shifts and/or specific events? Findings show an assortment of differences and connections across Twitter sentiments (fear, anger, and joy) based on policy measures and geographies during the COVID-19 pandemic. Results can be used by policy makers to quantify the satisfactory level of positive/negative acceptance of decision makers and identify important topics related to COVID-19 policy measures, which can be useful for imposing geographically varying lockdowns and protective measures using historical data.

Does online investor sentiment explain analyst recommendation changes? Evidence from an emerging market

PurposeThe authors examine how financial analysts respond to online investor sentiment when updating recommendations for specific stocks in South Africa. The aim is to establish whether online sentiment contains significant information that can influence analyst recommendations. The authors follow up the above by examining when online investor sentiment is most associated with analyst recommendation changes.Design/methodology/approachFor online investor sentiment proxies, the authors make use of the social media sentiment and news media sentiment scores provided by Bloomberg Inc. The sample size includes all companies listed on the Johannesburg Stock Exchange All Share Index. The study uses traditional ordinary least squares to examine the relation at the mean and quantile regression to identify the scope of the relationship across the distribution of the dependent variable.FindingsThe authors find evidence that pre-event news sentiment significantly influences analyst recommendation changes while no significant relationship is found with the Twitter sentiment. Further analysis shows that news sentiment is more influential when the recommendation changes are moderate (in the middle of the conditional distribution of the recommendation changes).Originality/valueThe study is the one of the first to examine the association between online sentiment and analyst recommendation changes in an emerging market using high frequency data. The authors also make a direct comparison between social media sentiment and news media sentiment, some of the most used contemporary investor sentiment proxies.