We also describe the process of preparing the dataset and present its analysis, including entity and annotator bias analysis, and some insights into possible challenges of the task of entity-level analysis of the news.doi:10.1016/j.procs.2021.09.136Katarzyna Baraniak...
2. data.csv/data.parquet: The main dataset containing stock price, trade volume, news events and news sentiment for S&P 500 companies during the period Oct 2020-Jul 2022. Below we’ve included a few visualisations to help you get a better understanding of the SNES da...
DL is built explicitly for dealing with significant amounts of data and performing complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve their trading process. More...
If you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Python code for rule-based sentiment analysis engine) in your research, please cite the above paper. For example: Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based...
There is a collection of sentiment analysis datasets assembled by the Interest Group on German Sentiment Analysis. However, to my knowlege, no german topic classification dataset is avaliable to the public. Due to grammatical differences between the English and the German language, a classifyer might...
Section four describes the research methodology and steps for sentiment analysis and machine learning for detection. Section five presents the research performed using the dataset along with the results obtained after comparing the framework proposed and the existing models. This is followed by the ...
Our objective is to provide the research community with adaptable and precise classification models adept at identifying fake news for the elections agenda. Quantitative evaluations of fake news classifiers on our dataset reveal that, while state-of-the-art LMs exhibit a slight edge over traditional ...
To explore past news and their scores, each individual news item is scored. Across time, it lets the user understand how the news scoring model performed on the whole dataset. 5. Stock Price Analytics The covariance matrix of stock price log returns can be reduced using PCA. Using this deco...
The sentiment polarity was obtained based on an ontology built from the analysis of the current dataset. If a word does not occur in the lexicon but is annotated in the ontology, the sentiment polarity of this word would be determined by its sentiment polarity annotated in the ontology. Their...
We consider this dataset a good choice due to its size and the number of researchers who used Sentiment140 for sentiment analysis [50], [61], [65]. The training set of the Sentiment140 accessed via the Hugging Face library contains encoded labels for positive and negative sentiment. To ...