The large number of internet users caused increasing the number of social media users. Twitter is one of social media that have a large number of users in Indonesia. As a social media, twitter allows users to share information via status in a tweet. Due to the limitations of the use of text is only 280 characters, emoticons are commonly used in tweet. Emoticon can explain the condition or feeling which is described in a text-shaped punctuation mark. This paper will focus on creating emoticon dictionary and weighting of an emoticon. Emoticon dictionary contains a list of 384 emoticons describing a variety of feelings and emotions. The used dataset contains Indonesian language tweets from twitter API. We tried to analyze sentiment on existing datasets with reference scores in SentiWordNet. Weighting emoticons done under the assumption that the emoticons have more effect in a sentence than ordinary words. After that, we classify the results into three classes, namely sentiment positive, negative and neutral. We compared the results between the emoticon-based algorithm and without considering emoticons algorithm. Accuracy obtained on the emoticon-based using algorithm is 0.74.