The study explores Twitter Sentiment Analysis (TSA) using Natural Language Processing (NLP) to understand societal views, trends, and feelings. The research involves data collection, pre-processing, feature extraction, sentiment analysis, model construction, and visualization. The data is then processed to address issues like extraneous characters, capitalization, and data errors. Sentiment analysis categorizes tweets into positive and negative. The NLP algorithm is central to sentiment classification, and the paper builds, trains, and assesses various machine learning models. Word clouds depict sentiment distribution, identify frequently occurring phrases, and emphasize sentiment trends over time. Potential roadblocks include data quality issues, careful algorithm selection, and model dependability. The sentiment analysis model demonstrated good accuracy and a balanced F1-score, demonstrating its competency in sentiment categorization. The study contributes to the emerging discipline of sentiment analysis by demonstrating how valuable insights can be extracted from massive amounts of social media data.

The inhibition efficacies of extracts of Cnidoscolus aconitifolius (Chaya) leaves in the corrosion of mild steel were investigated in H2SO4, and NaCl media. Weight loss method was employed to determine corrosion rate. The ribbed mild steel rod was cut into forty (40) coupons, each measuring 16 mm (diameter) by 8 mm (length) and their initial weights taken and noted. Twenty (20) beakers containing 0.5M and 1.0M each of H2SO4 and NaCl solution separately with varying volumes of the extracts ranging from 5ml to 20ml were set up with four (4) coupons in each beaker fully immersed using nylon strings. The set up was allowed to stand for an exposure time of 672hrs with a coupon withdrawn from each beaker every 168 hours and processed using standard procedures before being reweighed using digital weighing balance. The weight difference for each coupon was obtained and used for corrosion rate calculation using the formula, CPR= K∆W/ρAt. Other corrosion parameters were also obtained using the relevant formulae: inhibition efficiency, IE%=(〖CR〗_c-〖CR〗_i)/〖CR〗_c ×100 and surface coverage, , θ=(〖CR〗_c-〖CR〗_i)/〖CR〗_c .The results obtained indicated that the normal corrosion rate profile for passivating metals was followed with the weight loss by the coupons being higher in the control media than those ones in the inhibited media. It was also observed that increase in volumes of these extracts from 5ml to 20ml decreased the corrosion rate of mild steel in the media. We found that in both media Cnidoscolus aconitifolius (Chaya) exhibited high inhibition efficiencies of 78.14% in H2SO4 and 64.15% in NaCl at extract concentration of 20 ml. Plots of the Langmuir adsorption isotherms suggest uniform surface covering of the adsorbent molecules at equilibrium state of the adsorbate–adsorbent system, with very strong adsorption forces. Based on these findings, the results suggest that Cnidoscolus aconitifolius (Chaya) leaves extract is a veritable green inhibitor for both acidic and salt media and can be a suitable alternative to the synthetic inhibitors widely used in the oil and gas industry.

In recent years, in order to innovate teaching methods and apply information technology to teaching, Hung Vuong University has applied an online teaching and learning model. However, this model has only been widely deployed to all sectors in the school when the nCov-19 epidemic broke out. This is a teaching method commonly applied in universities today. However, to be able to maximize the efficiency and quality of this model is not easy. This article proposes some solutions to improve the quality of online teaching and learning at Hung Vuong University.