Among the plethora of social media, Twitter has emerged as the favorite destination for researchers in recent times. Many researchers are inclined to work on Twitter due to the availability of massive tweets and its unique features like hashtags and short messages. In recent times, various studies have preferred the hashtags (#sarcasm and #sarcastic) to collect Twitter dataset for sarcasm detection. However, hashtag-based distant supervision suffers from the problem of the inclusion of null tweets in the datasets which can be considered as a critical one for sarcasm detection. In this article, an algorithm is proposed for automatic detection and filtration of null tweets in the Twitter data. Additionally, an algorithm to identify sarcastic tweets using context within a tweet is also proposed. This approach use dictionaries of handpicked hashtag words, emoticons as the context within a tweet. Finally, we deployed a rule-based algorithm to analyse the performance of the proposed approach. The proposed approach attains the accuracy of 97.3% (after filtering null tweets) and 83.13% (without filtering null tweets) using a rule-based approach. The attained results conclude that after elimination of null tweets, the performance of the proposed system improved significantly.