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Company specific results of the sentiment analysis using TextBlob. The time period stands between 2020-10-27 and 2020-11-14. (a) Results by Companies and (b) Aggregate Sentiment Result.
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The prediction and speculation about the values of the stock market especially the values of the worldwide companies are a really interesting and attractive topic. In this article, we cover the topic of the stock value changes and predictions of the stock values using fresh scraped economic news about the companies. We are focussing on the headline...
Contexts in source publication
Context 1
... Figure 3 shows that sentiment values separated by companies. No other value can approach the neutral section, it can be concluded that the analysis of the given economic news headlines and its outcome is very uncertain. ...
Context 2
... other value can approach the neutral section, it can be concluded that the analysis of the given economic news headlines and its outcome is very uncertain. In the case of AMD, it can be noted that in Figure 3(a), in addition to the 63 news headlines rated as neutral, 31 are positive and 5 are negative. In the case of FB --Facebook, in addition to the 80 news headlines rated as neutral, there are 13 positive and 6 negative values as well. ...
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The spread of COVID-19 in the world had a devastating impact on the world economy, trade relations, and globalization. As the pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in a similar crisis. The COVID-19 p...
Citations
... While specifically attuned to social media, VADER also performs well in other domains. Some notable studies that have utilized VADER in the financial domain include [2,24,[55][56][57]. ...
The importance of sentiment analysis in the rapidly evolving financial markets is widely recognized for its ability to interpret market trends and inform investment decisions. This study delves into the target-level financial sentiment analysis (TLFSA) of news headlines related to stock. The study compares the performance in the TLFSA task of various sentiment analysis techniques, including rule-based models (VADER), fine-tuned transformer-based models (DistilFinRoBERTa and Deberta-v3-base-absa-v1.1) as well as zero-shot large language models (ChatGPT and Gemini). The dataset utilized for this analysis, a novel contribution of this research, comprises 1476 manually annotated Bloomberg headlines and is made publicly available (due to copyright restrictions, only the URLs of Bloomberg headlines with the manual annotations are provided; however, these URLs can be used with a Bloomberg terminal to reconstruct the complete dataset) to encourage future research on this subject. The results indicate that the fine-tuned Deberta-v3-base-absa-v1.1 model performs better across all evaluation metrics than other evaluated models in TLFSA. However, LLMs such as ChatGPT-4, ChatGPT-4o, and Gemini 1.5 Pro provide similar performance levels without the need for task-specific fine-tuning or additional training. The study contributes to assessing the performance of LLMs for financial sentiment analysis, providing useful insights into their possible application in the financial domain.
... However, compared with the study of emotion in other text genres we encounter in modern life, such as news articles (Nemes and Kiss, 2021) and reviews (Yi and Liu, 2020), there is less research on poetry (Ahmad et al., 2020a), not to mention on classical Chinese poetry (Tang et al., 2020). Actually, it is more challenging to process ancient Chinese texts than modern Chinese text. ...
Classical Chinese poetry has a long history, dating back to the 11th century BC. By investigating the sentiment expressed in the poetry, we can gain more insights in the emotional life and history development in ancient Chinese culture. To help improve the sentiment analysis performance in the field of classical Chinese poetry, we propose to utilize the unique information from the individual short lines that compose the poem, and introduce a multi-task framework with hierarchical attention enhanced with short line sentiment labels. Specifically, the multi-task framework comprises sentiment analysis for both the overall poem and the short lines, while the hierarchical attention consists of word- and sentence-level attention, with the latter enhanced with additional information from short line sentiments. Our experimental results showcase that our approach leveraging more fine-grained information from short lines outperforms the state-of-the-art, achieving an accuracy score of 72.88% and an F1-macro score of 71.05%.
... Research shows that Artificial Neural Networkxs (ANNs) can predict stock prices with a Mean Absolute Percentage Error (MAPE) of around 2% [3]. Additionally, techniques such as sentiment analysis have proven effective in predicting stock market trends [4,5]. Despite the focus on complex algorithms, simpler models like linear regression, Auto-Regressive, and Moving Average models (ARMA) continue to yield satisfactory results in stock prediction [6,7,8]. ...
... Proceeding of The 5 th Asia-Pacific Management Research Conference (APMRC) 2024:Adapting Sustainability Strategies in Business and Management, pp.[1][2][3][4][5][6][7][8] ...
... Adapting Sustainability Strategies in Business and Management, pp. [1][2][3][4][5][6][7][8] ...
... Te wealth of information discussed in stock forums is a valuable resource for investors, who may make diferent investment decisions based on the content of the forums. Tis refects the connection between sentiment and stock price prediction; however, a literature review revealed that few scholars had studied their relationship during the COVID-19 pandemic, possibly because most countries afected by COVID- 19 have remained in a bear market. Te economic impact of COVID-19 and the market's growth are diferent among countries [5]. ...
... Much research has focused on news outlets [18,19] and social networking services [20] as sources of information for sentiment analysis and stock price predictions. Tere are diferent advantages and disadvantages of using various sources of semantic analysis to predict stock prices (see Table 1). ...
During the COVID-19 pandemic, the stay-at-home and biotechnology economies played a big part in economic development. The major internet forums have received more attention and discussions concerning stocks related to biotechnology and the stay-at-home economy. When the number of confirmed COVID-19 cases in a country rises, so does the positive or negative sentiment in stock commentaries. Whether stock forums can be a valuable source of information for investors has become a subject of academic research. This study used text mining and sentiment analysis to analyze stock forum articles, classify daily reports into emotion and investment orientation indicators, and correlate these indicators with the next-day 2019’s stock prices. The findings indicate a positive correlation between stock forum articles and stock prices. Additionally, this research enriched the case of sentiment analysis in the context of Chinese sentiment. This study contributes not only to academic reference and refinement but also to market investors’ judgment.
... Moreover, the predictive power of news sentiment was further explored by comparing different NLP tools, including BERT and RNN, against traditional sentiment analysis tools like VADER and TextBlob in forecasting stock market movements based on news headlines. [11] The study found that more sophisticated models like BERT provided deeper insights into sentiment trends, correlating closely with actual stock market changes. ...
This paper presents a novel multimodal deep learning framework for analyzing news sentiments and forecasting market movements by leveraging natural language processing, deep learning, and auxiliary data sources. Traditional methods often rely solely on textual news data, limiting their predictive power due to the complexity and ambiguity of language. Our approach incorporates additional modalities such as stock prices, social media sentiment, and economic indicators to capture a more comprehensive view of market dynamics. We employ a hybrid deep learning architecture that combines convolutional neural networks (CNNs) for text feature extraction, long short-term memory (LSTM) networks for capturing sequential dependencies, and attention mechanisms to selectively focus on the most relevant features. To address data scarcity, we introduce advanced data augmentation techniques, generating synthetic news headlines based on historical stock price movements and sentiment patterns. The proposed system is evaluated on a comprehensive dataset spanning multiple years, including news headlines, stock prices, social media data, and economic indicators. Our method achieves an accuracy of 77.51%, significantly outperforming traditional methods and demonstrating improved robustness and predictive power. This study highlights the potential of integrating diverse data sources and sophisticated deep learning techniques to enhance news sentiment analysis and market movement forecasting.
... Nemes and Kiss [5] employed several sentiment analysis algorithms to emotionally inspect and categorize different news headlines from economics and assess their influence on distinct stock market value movements even when the context was missing. Emotions were divided into three categories: good, negative, and neutral. ...
... neutral, 36.7% positive, and 17.3% negative with TextBlob. Nemes and Kiss (2021) performed sentiment analysis on company news headlines using BERT, VADER, TextBlob, and a recurrent neural network (RNN), finding BERT and the RNN more accurate at identifying stock value change timings by comparing sentiment results with stock changes, noting significant differences in the impact of emotional values across models. ...
Predicting stock market movement direction is a challenging task due to its fuzzy, chaotic, volatile, nonlinear, and complex nature. However, with advancements in artificial intelligence, abundant data availability, and improved computational capabilities, creating robust models capable of accurately predicting stock market movement is now feasible. This study aims to construct a predictive model using news headlines to predict stock market movement direction. It conducts a comparative analysis of five supervised classification machine learning algorithms—logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN)—to predict the next day’s movement direction of the close price of the Nepal Stock Exchange (NEPSE) index. Sentiment scores from news headlines are computed using the Valence Aware Dictionary for Sentiment Reasoning (VADER) and TextBlob sentiment analyzer. The models’ performance is evaluated based on sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). Experimental results reveal that all five models perform equally well when using sentiment scores from the TextBlob analyzer. Similarly, all models exhibit almost identical performance when using sentiment scores from the VADER analyzer, except for minor variations in AUC in SVM vs. LR and SVM vs. ANN. Moreover, models perform relatively better when using sentiment scores from the TextBlob analyzer compared to the VADER analyzer. These findings are further validated through statistical tests.
... The research uses content analysis to identify critical topics from the storyboard and narrative of cultural documentaries about Sumba, Indonesia. Furthermore, it seeks to analyze viewer responses through toxicity and sentiment classification using VADER [24]- [26]. By employing these methodological approaches, the study endeavors to uncover the thematic underpinnings of Sumba's cultural narratives as portrayed in documentary videos while shedding light on audiences' emotional and evaluative reactions [27]- [30]. ...
This research explores sentiment classification and toxicity assessment in cultural documentary videos through a systematic analysis framework based on the Cross-Industry Standard Process for Data Mining (CRISP-DM). The study evaluates the sentiment polarity of viewer comments by utilizing a diverse array of machine-learning algorithms, including k-NN, DT, NBC, and SVM. It identifies toxic language patterns across multiple videos. Additionally, the research employs SMOTE to address class imbalance issues and enhance model performance. The results reveal high accuracy rates ranging from 72.24% to 96.79% in sentiment classification, indicating the effectiveness of the proposed methodology. Moreover, toxicity analysis unveils varying degrees of toxic language prevalence, with toxicity scores ranging from 0.01270 to 0.09334 across different videos. Despite these achievements, the study acknowledges the inherent limitations of toxicity scoring algorithms in capturing contextual nuances. Overall, this research contributes to understanding sentiment dynamics and toxicity trends in cultural documentary content and underscores the importance of employing advanced machine learning techniques within a structured analytical framework for insightful data interpretation and decision-making.
... Several studies have demonstrated that LLM based models obtain far better accuracy scores when compared to more traditional ML based Sentiment Analysis models for a wide range of applications. Furthermore, LLM based models are capable of tackling more nuanced problem statements [44,45] Ranging from the prediction of stock prices based on twitter sentiment [46] to accurately predicting the sentiment of a given news headline [47], studies have shown that LLMs have performed exceedingly well in all these areas. ...
... The optimal hyperparameters for our Logistic Regression model were determined to be solver='saga' and C=4. 46. The selection of the solver parameter is critical, as 'saga' is particularly adept at handling the challenges posed by high-dimensional datasets such as ours, providing robust support for both L1 and L2 regularization. ...
Confusion, fear and mixed sentiments prevail in the minds of people towards what is arguably one of the most important of dynamics of modern human society: Artificial Intelligence (AI). This study aims to explore the contributions of news media towards this phenomenon - we analyze nearly seventy thousand recent news headlines on AI, using natural language processing (NLP) informatics methods, machine learning (ML) and large language models (LLMs) to draw insights and discover dominant themes. Our theoretical framework was derived from extant literature which posits the power of fear producing articles and news headlines which produce significant impacts on public behavior even when available in small quantities. We applied extensive textual informatics methods using word and phrase frequency analytics, sentiment analysis and human experts based thematic analysis to discover insights on AI phobia inducing news headlines. Our rigorous analysis of nearly seventy thousand headlines using multiple validation methods in NLP (exploratory informatics including BERT, Llama 2 and Mistral based topic identification), ML (supervised informatics) and LLMs (neural nets for sentiment classification, with BERT, Llama 2 and Mistral) demonstrates the presence of an unreasonable level of emotional negativity and fear inducing verbiage in AI news headlines. The framing of AI as being dangerous or as being an existential threat to humanity can have a profound impact on public perception, and the resulting AI phobia and confusion in public perceptions are inherently detrimental to the science of AI. Furthermore, this can also impact AI policy and regulations, and harm society. We conclude with a discussion deducing implications for society and make recommendations for education and policies that could support human identity and dignity.
... Furthermore, LLM based models are capable of tackling more nuanced problem statements. (44) (45) Ranging from the prediction of stock prices based on twitter sentiment (46) to accurately predicting the sentiment of a given news headline (47), studies have shown that LLMs have performed exceedingly well in all these areas. ...
... The optimal hyperparameters for our Logistic Regression model were determined to be solver='saga' and C=4. 46. The selection of the solver parameter is critical, as 'saga' is particularly adept at handling the challenges posed by high-dimensional datasets such as ours, providing robust support for both L1 and L2 regularization. ...
Confusion, fear and mixed sentiments prevail in the minds of people towards what is arguably one of the most important of dynamics of modern human society: Artificial Intelligence (AI). This study aims to explore the contributions of news media towards this phenomenon-we analyze nearly seventy thousand recent news headlines on AI, using natural language processing (NLP) informatics methods, machine learning (ML) and large language models (LLMs) to draw insights and discover dominant themes. Our theoretical framework was derived from extant literature which posits the power of fear producing articles and news headlines which produce significant impacts on public behavior even when available in small quantities. We applied extensive textual informatics methods using word and phrase frequency analytics, sentiment analysis and human experts based thematic analysis to discover insights on AI phobia inducing news headlines. Our rigorous analysis of nearly seventy thousand headlines using multiple validation methods in NLP (exploratory informatics including BERT, Llama 2 and Mistral(1; 2) based topic identification), ML (supervised informatics) and LLMs (neural nets for sentiment classification, with BERT, Llama 2 and Mistral) demonstrates the presence of an unreasonable level of emotional negativity and fear inducing verbiage in AI news headlines. The framing of AI as being dangerous or as being an existential threat to humanity can have a profound impact on public perception, and the resulting AI phobia and confusion in public perceptions are inherently detrimental to the science of AI. Furthermore, this can also impact AI policy and regulations, and harm society. We conclude with a discussion deducing implications for society and make recommendations for education and policies that could support human identity and dignity.
