Most of the previous studies on the semantic analysis of social media feeds have not considered the issue of ambiguity that is associated with slangs, abbreviations, and acronyms that are embedded in social media posts. These noisy terms have implicit meanings and form part of the rich semantic context that must be analysed to gain complete insights from social media feeds. This paper proposes an improved framework for pre-processing of social media feeds for better performance. To do this, the use of an integrated knowledge base (ikb) which comprises a local knowledge source (naijalingo), urban dictionary and internet slang was combined with the adapted Lesk algorithm to facilitate semantic analysis of social media feeds to resolve the ambiguity in the usage of slangs/acronyms/abbreviations. Experimental results showed that the proposed approach performed better than existing methods when it was tested on three machine learning models, which are support vector machines, multilayer perceptron, and convolutional neural networks. The framework had an accuracy of 94.07% on a standardized dataset, and 99.78% on localised dataset when used to extract sentiments from tweets. The improved performance on the localised dataset reveals the advantage of integrating the use of local knowledge sources into the process of resolving social media feeds particularly in handling slangs/acronyms/abbreviations that have contextually rooted meanings.