Sentiment Analysis has become a popular area of research due to exponential increase in the volume of user generated content with the rise of Internet and Social Networking Websites. Sentiment Analysis can be performed at different level of granularities such as document level, sentence level and aspect level. Aspect level sentiment analysis provides more deeper insights on how customer feels about product/service. Aspect term extraction, aspect category detection and sentiment polarity detection are the core tasks in aspect based opinion mining. This report presents existing state-of-the-art approaches for performing aspect level opinion mining. We have also provided a comprehensive list of publicly available lexicons and annotated datasets that can be used to build aspect level sentiment analysis systems. The first chapter introduces the research area of Opinion Mining. The second chapter focuses on aspect level opinion mining. In the next three chapters, we have discussed approaches of aspect term extraction, aspect category detection and aspect polarity identification. Taxonomy of various approaches discussed in literature related to opinion mining is presented in Chapter 6. Chapter 7 presents evaluation metrics and various publicly available lexicons and datasets. Chapter 8 provides details of our tool named ABSA Toolkit developed using state-of-art techniques of aspect level opinion mining. The report concludes with some emerging techniques and research issues in opinion mining