Johnson Kinyua

The SETSCI Conference Proceedings is an Open Access publication created for the storage and indexing of proceedings | We developed a series of financial social media sentiment analysis models starting with PyFin-Sentiment, a recent state-of-the-art domain-specific large language model for the financial domain. We focused on label accuracy and improvements in the quality and quantity of the dataset by collecting more data. We were able to improve model accuracy from 0.70 to 0.93, supporting a well-known maxim that models are just as good as the data they are trained on. This created a consistently accurate model that outperformed other existing financial sentiment models. We also used nuanced approaches such as separating social media data into short and long comments which drastically improved the accuracy of our model.

Predicting the directions of financial markets has been performed using a variety of approaches, and the large volume of unstructured data generated by traders and other stakeholders on social media microblog platforms provides unique opportunities for analyzing financial markets using additional perspectives. Pretrained large language models (LLMs) have demonstrated very good performance on a variety of sentiment analysis tasks in different domains. However, it is known that sentiment analysis is a very domain-dependent NLP task that requires knowledge of the domain ontology, and this is particularly the case with the financial domain, which uses its own unique vocabulary. Recent developments in NLP and deep learning including LLMs have made it possible to generate actionable financial sentiments using multiple sources including financial news, company fundamentals, technical indicators, as well social media microblogs posted on platforms such as StockTwits and X (formerly Twitter). We developed a financial social media sentiment analyzer (FinSoSent), which is a domain-specific large language model for the financial domain that was pretrained on financial news articles and fine-tuned and tested using several financial social media corpora. We conducted a large number of experiments using different learning rates, epochs, and batch sizes to yield the best performing model. Our model outperforms current state-of-the-art FSA models based on over 860 experiments, demonstrating the efficacy and effectiveness of FinSoSent. We also conducted experiments using ensemble models comprising FinSoSent and the other current state-of-the-art FSA models used in this research, and a slight performance improvement was obtained based on majority voting. Based on the results obtained across all models in these experiments, the significance of this study is that it highlights the fact that, despite the recent advances of LLMs, sentiment analysis even in domain-specific contexts remains a difficult research problem.

The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspectives. The meme stock mania of 2021 brought together stock traders and investors that were also active on social media. This mania was in good part driven by retail investors’ discussions on investment strategies that occurred on social media platforms such as Reddit during the COVID-19 lockdowns. The stock trades by these retail investors were then executed using services like Robinhood. In this paper, machine learning models are used to try and predict the stock price movements of two meme stocks: GameStop ($GME) and AMC Entertainment ($AMC). Two sentiment metrics of the daily social media discussions about these stocks on Reddit are generated and used together with 85 other fundamental and technical indicators as the feature set for the machine learning models. It is demonstrated that through the use of a carefully chosen mix of a meme stock’s fundamental indicators, technical indicators, and social media sentiment scores, it is possible to predict the stocks’ next-day closing prices. Also, using an anomaly detection model, and the daily Reddit discussions about a meme stock, it was possible to identify potential market manipulators.

In silico identification and characterization of vaccine antigens has opened up new frontiers in the field of reverse vaccinology to mitigate the effects of livestock diseases by development of new subunit vaccines. This study aims to characterize, express and purify MSC_0265 for eventual use in immunoassays and inoculation in goats. Mycoplasma mycoides subs. mycoides (Mmm) and Mycoplasma capricolum capripneumoniae (Mccp) are similar pathogens on the genomic level and are the causative agents of Contagious Bovine Pleuropneumonia (CBPP) in cattle and Contagious Caprine Pleuropneumonia (CCPP) in goats respectively. In this study, BLAST was used to identify the homology of MSC_0265 in Mycoplasma capricolum capripneumoniae genome and the protein it is similar to. Characterization of MSC_0265 was also done using I-TASSER to predict secondary structure, solvent accessibility, normalised B-factor, 3D models and function. With cut off points of 0.0 for E-value, 100% for Query coverage and 90% for Identity, MSC_0265 a pyruvate dehydrogenase enzyme gave a high homology score on tBLASTn and BLASTp. It had earlier been cloned in pGS21a vector before proceeding with expression and purification of the His-tagged protein by Ni-NTA affinity chromatography. This study identified the homologue of MSC_0265 as protein WP_029333261.1 in the Mycoplasma capricolum capripneumoniae genome (Accession NZ_LN515398.1) using tBLASTn and BLASTp. Additionally, MSC_0265 was characterized and its optimal expression profile and estimated molecular weight verified.

Due to the high sensitivity of RFID tag-reader performance to the operating environment, RFID data streams generated are unreliable and contain a significant amount of missed readings. RFID data cleaning is therefore an essential task for successful deployment of RFID systems. One of the common techniques used by RFID middleware systems to compensate for the missed readings is the use of sliding-window filters. However, setting an optimum window size is non-trivial task especially in mobile tag environments. In this paper we present a new adaptive data cleaning scheme called WSTD based on some of the concepts proposed in SMURF but with an improved transition detection mechanism. WSTD uses the comparison of the two window subrange observations or estimated tag counts to detect when transitions occur within a window. In the mobile environment, our experimental results show that the WSTD scheme performs better than SMURF producing an improvement of about 30% less overall errors than that produced by SMURF.