Effect of public sentiment on stock market movement prediction during the COVID-19 outbreak.

Forecasting the stock market is one of the most difficult undertakings in the financial industry due to its complex, volatile, noisy, and nonparametric character. However, as computer science advances, an intelligent model can help investors and analysts minimize investment risk. Public opinion on social media and other online portals is an important factor in stock market predictions. The COVID-19 pandemic stimulates online activities since individuals are compelled to remain at home, bringing about a massive quantity of public opinion and emotion. This research focuses on stock market movement prediction with public sentiments using the long short-term memory network (LSTM) during the COVID-19 flare-up. Here, seven different sentiment analysis tools, VADER, logistic regression, Loughran-McDonald, Henry, TextBlob, Linear SVC, and Stanford, are used for sentiment analysis on web scraped data from four online sources: stock-related articles headlines, tweets, financial news from "Economic Times" and Facebook comments. Predictions are made utilizing both feeling scores and authentic stock information for every one of the 28 opinion measures processed. An accuracy of 98.11% is achieved by using linear SVC to calculate sentiment ratings from Facebook comments. Thereafter, the four estimated sentiment scores from each of the seven instruments are integrated with stock data in a step-by-step fashion to determine the overall influence on the stock market. When all four sentiment scores are paired with stock data, the forecast accuracy for five out of seven tools is at its most noteworthy, with linear SVC computed scores assisting stock data to arrive at its most elevated accuracy of 98.32%.

Predictability by Box-Behnken model for carbaryl adsorption by soils of Indian origin.

This study was undertaken to investigate the adsorption capacity of carbaryl on four Indian soils with different physiochemical properties. A batch adsorption study was carried out in order to evaluate the maximum adsorption capacity of carbaryl using a Response Surface Methodology (RSM). The effects of operating parameter such as initial carbaryl concentration (1-20 mgL⁻¹), adsorbent dosage (0.5-6 g) and contact time (10-180 min) were examined. The proposed quadratic model for Box-Behnken design fits very well to the experimental data because it may be used to navigate design space according to ANOVA results. The regression co-efficient (R²) of the models developed and the results of validation experiments conducted at optimal conditions strongly suggests that the predicted values are in good agreement with experimental results. Contour and response surface plots are used to determine the interactions effects of main factors and optimal conditions of the process. The experiment can be utilized as a guideline for better understanding of carbaryl adsorption onto soil under different operating conditions. The results show that the forest soil is most efficient in binding carbaryl (Sevin) than the other types of soil tested.