## 3 Methodology
### Dataset Creation and Annotation  
To conduct a comprehensive examination in this research, news headlines pertaining to well-known stock companies, namely Amazon, Netflix, Nvidia, and Alphabet, were collected. These headlines were sourced from Bloomberg's financial news service, Bloomberg Terminal. Bloomberg is a well-acknowledged platform that is extensively utilized for consolidating financial news data from many sources [50, 51, 52]. The dataset encompasses the time period from 1 September 2023, to 31 January 2024. The dataset consists of 1476 distinct news headlines, which are each linked to a particular stock company (referred to as the \"target\"), a timestamp, and a URL. Keywords relevant to the targeted companies, such as \"target-company\" and \"target-company in headlines\", were used during the data-collecting phase from the Bloomberg Terminal. For example, when specifically analyzing Amazon.com, Inc. as the subject company, keywords such as 'Amazon.com Inc.' and 'Amazon in headlines' were employed to aid in extracting pertinent articles. As a result, this method produced a collection of news articles that were pertinent to the pre-identified target companies. These articles had headlines that mentioned these companies. Subsequently, the gathered articles underwent processing to extract crucial information such as headlines, timestamps, and URLs, which were later recorded in a database, as seen in Figure 1.  
To determine the dataset's dependability and confirm the experimental evaluation, each headline is manually annotated to evaluate the sentiment toward the considered target company. This technique involves examining the sentiment expressed in the headlines in relation to the set targets and categorizing these sentiments as positive, negative, or neutral. The annotations are performed by three annotators, experts in finance and economics, all having a high level of English proficiency (CEFR level C1). The annotators are given comprehensive instructions that outline the tasks required for target-level annotation. Here are some examples that demonstrate the annotation guidelines given to the annotators (the full version of the guidelines is released together with the dataset):  
* Assess the sentiment of each headline exclusively with respect to the target rather than considering the overall sentiment of the entire headline. Annotators should assume the viewpoint of an investor and evaluate the probable influence of the headline on investment decisions. * Assign a sentiment score to a headline in relation to the target, using the following scale: (0) for a neutral sentiment, (\(-1\)) for a negative sentiment, and (\(+1\)) for a positive sentiment. * Formulate annotations based solely on the sentiment directly conveyed in the headline rather than relying on external information. * Assign a neutral sentiment score of 0 to headlines that contain muddled sentiments, uncertainty, or ambiguity. * Assign a neutral sentiment when there is no evident positive or negative sentiment regarding the target. The manual annotation technique for news headlines involves an initial phase when the three annotators individually examine the same subset of 150 headlines. To quantify the level of agreement among the three coders and ensure the consistency of the manual labeling process, we employ [PERSON]'s Alpha [53] as a measure of intercoder agreement. The calculated [PERSON]'s Alpha score is 0.8188, which indicates a satisfactory level of agreement among the coders, thus confirming the reliability of the contributed annotations. After ensuring the satisfactory level of intercoder agreement, the remaining headlines, which amount to 1326, are divided equally among the three raters. Each rater is assigned the task of personally annotating 442 headlines. Table 1 presents examples of annotated headlines. Moreover, Figure 2 shows the distribution of sentiment specific to each target. Overall, the dataset is slightly unbalanced with roughly 49% of positive headlines, 28% of negative headlines, and 23% of neutral headlines.