Accumulation of large amount of location-specific reviews on web due to escalating popularity of Location-based Social Networking platforms like Yelp, Foursquare, Brightkite etc. in recent years, has created the opportunity to discover location-specific activities and develop myriads of location-aware activity recommendation applications. The performance and popularity of such recommendation applications greatly depend on the richness and accuracy of the back-end knowledgebase, which intern is regulated by information relevancy and redundancy issues. Existing work on activity discovery have not made any attempt to ensure relevancy and non-redundancy of discovered knowledge (i.e., location-specific activities). Moreover, majority of these work have utilized body-worn sensors, images or human GPS traces and discovered generalized activities that do not convey any location-specific knowledge. In this thesis, we address the mentioned issues with serious concern and propose an effective solution to discover Location-specific Activity Network, in short LANet from location-aware reviews. The information network LANet serves as an accurate, enriched and unified knowledgebase of a Location-aware Activity Recommendation System. While building LANet, we also introduce novel ideas like, activity-based location similarity detection and measuring uniqueness, generality/speciality of an activity at a particular location to enrich the said knowledge base to a great extent. Experimental results show the information richness and accuracy of the proposed knowledge base which is comparable to human perception and accounts for our success in achieving the desired solution.