Twitter data mining techniques have been used in the run-up to elections to predict their outcomes and perform analysis to explain results. Due to the popularity of the social media platform it is possible to collect large amounts of data with which often lexicon-based sentiment analysis has been used to accomplish these tasks, mostly because of its efficiency and simplicity. More recently, hybrid techniques, which in addition to calculating tweet sentiment also incorporate topic modelling methods to extract the main “topics” from a corpus of text, have been applied independently for both election prediction and analysis. It is possible to use hybrid methods to analyse different political issues (e.g. economic, social, etc) and the public opinion for candidates in respect to them; and other hybrid methods have been shown to outperform baseline sentiment analysis approaches for election prediction. A mining solution which can accomplish both of these tasks non-exhaustively is desirable for better predictions and a greater understanding of election outcomes. This report will present a novel approach to mining Twitter data, Hybrid Topic-Based Sentiment Analysis with Issue Filtering (HTBSA*), which will not only pose as a potential improvement upon state-of-the-art techniques for election prediction; but can be abstracted to perform candidate analysis on any individual political issue, proposing a baseline methodology for doing this. This research approach has effectively outperformed all of the well-established methods in the realm of lexicon-based election prediction, giving a mean average error as low as 2.20% from true vote share. This technique was performed on data collected on the run up to the UK General Election 2019 and in an addition to this, it has successfully been black box tested on an unseen dataset. Based on the empirical evidence given by our results, HTBSA* can be relied upon to predict elections occurring in the future, but analysis results in respect to individual political issues may be inconsistent, suggesting further work is required. Lines of research that come as a result of this study have the potential to tackle election mining problems in new ways, which are more sophisticated than what has been done previously.