Sentiment analysis (SA) is a technique of textual data that uses Natural Language Processing (NLP) and Machine Learning (ML) to evaluate text automatically for the writer’s feelings (positive, negative, and neutral). The lexicon-based approach is used to extracting sentiment from text and user reviews. In the sentiment analysis task, the sentiment lexicon, which offers sentiment polarity in terms, plays an important role. Most sentiment lexicons currently have only one polarity of sentiment for each word and disregard sentimental complexity. The problem of Sentiment Analysis was well studied and two main approaches were developed namely corpus-based and lexicon-based approaches. This paper discusses lexiconbased approaches to sentiment analysis. Contextual words, Acronyms, and emoticons are the major problems in sentiment analysis. The proposed techniques to improve the accuracy of sentiment analysis and also analyze the contextual words, acronyms, and emoticons.