In this study, we propose InSet, an Indonesian sentiment lexicon built to identify written opinion and categorize it into positive or negative opinion, which could be utilized to analyze public sentiment towards particular topic, event, or product. Composed using collection of words from Indonesian tweet, InSet was constructed by manually weighting each words and enhanced by adding stemming and synonym set. As the result, we obtained 3,609 positive words and 6,609 negative words with score ranging between -5 and +5. Based on the experiment utilizing the InSet, our method outperforms other rarely found Indonesian lexicon that we used as baseline.