U.S. President Joe Biden took his oath after being victorious in the controversial U.S. elections of 2020. The polls were conducted over postal ballot due to the coronavirus pandemic following delays of the announcement of the election’s results. Donald J. Trump claimed that there was potential rigging against him and refused to accept the results of the polls. The sentiment analysis captures the opinions of the masses over social media for global events. In this work, we analyzed Twitter sentiment to determine public views before, during, and after elections and compared them with actual election results. We also compared opinions from the 2016 election in which Donald J. Trump was victorious with the 2020 election. We created a dataset using tweets’ API, pre-processed the data, extracted the right features using TF-IDF, and applied the Naive Bayes Classifier to obtain public opinions. As a result, we identified outliers, analyzed controversial and swing states, and cross-validated election results against sentiments expressed over social media. The results reveal that the election outcomes coincide with the sentiment expressed on social media in most cases. The pre and post-election sentiment analysis results demonstrate the sentimental drift in outliers. Our sentiment classifier shows an accuracy of 94.58% and a precision of 93.19%.

Accurate channel state information (CSI) at the transmitter is an essential prerequisite for transmit beamforming in massive multiple input multiple output (MIMO) systems. However, due to a large number of antennas in massive MIMO systems, the pilot training and feedback overhead become a bottleneck. To resolve this issue, the research work presents a novel framework for frequency division duplex (FDD) based multi-user massive MIMO system. A 2-step quantization technique is employed at the user equipment (UE) and the CSI is recovered at the base station (BS) by applying the proposed compressed sensing (CS) based algorithms. The received compressed pilots are quantized by preserving 1 bit per dimension direction information as well as the partial amplitude information. Subsequently, this information is fed back to the BS, which employs the proposed quantized partially joint orthogonal matching pursuit (Q-PJOMP) or quantized partially joint iterative hard thresholding (Q-PJIHT) CS algorithms to recover the CSI from a limited and quantized feedback. Indeed, an appropriate dictionary and the hidden joint channel sparsity structure among users is exploited by the CS methods, resulting in the reduction of the feedback information required for channel estimation. Simulations are performed using singular value decomposition (SVD) and minimum mean square error (MMSE) beamforming utilizing the estimated channel. The results confirm that the proposed 2-step quantization approaches the system with channel knowledge without quantization, thus overcoming the training and feedback overhead problem. Moreover, the proposed 2-step quantization outperforms 1-bit quantization, at the cost of slightly higher complexity.