This article introduces a new general-purpose sentiment lexicon called WKWSCI Sentiment Lexicon and compares it with five existing lexicons: Hu & Liu Opinion Lexicon, Multi-perspective Question Answering (MPQA) Subjectivity Lexicon, General Inquirer, National Research Council Canada (NRC) Word-Sentiment Association Lexicon and Semantic Orientation Calculator (SO-CAL) lexicon. The effectiveness of the sentiment lexicons for sentiment categorisation at the document level and sentence level was evaluated using an Amazon product review data set and a news headlines data set. WKWSCI, MPQA, Hu & Liu and SO-CAL lexicons are equally good for product review sentiment categorisation, obtaining accuracy rates of 75%–77% when appropriate weights are used for different categories of sentiment words. However, when a training corpus is not available, Hu & Liu obtained the best accuracy with a simple-minded approach of counting positive and negative words for both document-level and sentence-level sentiment categorisation. The WKWSCI lexicon obtained the best accuracy of 69% on the news headlines sentiment categorisation task, and the sentiment strength values obtained a Pearson correlation of 0.57 with human-assigned sentiment values. It is recommended that the Hu & Liu lexicon be used for product review texts and the WKWSCI lexicon for non-review texts.