A novel Indonesian sentiment lexicon (SentIL -- Sentiment Indonesian Lexicon) is created with an automatic pipeline; from creating sentiment seed words, adding new words with slang words, emoticons, and from the given dictionary and sentiment corpus, until tuning sentiment value with tagged sentiment corpus. It begins by taking seed words from WordNet Bahasa that mapped with sentiment value from English SentiWordNet . The seed words are enriched by combining the dictionary-based method with words’ synonyms and antonyms, and corpus-based methods with word embedding for word similarity that trained in positive and negative sentiment corpus from online marketplaces review and Twitter data. The valence score of each lexicon is recalculated based on its relative occurrence in the corpus. We also add some famous slang words and emoticons to enrich the lexicon. Our experiment shows that the proposed method can provide an increase of 3.5 times lexicon number as well as improve the accuracy of 80.9% for online review and 95.7% for Twitter data, and they are better than other published and available Indonesian sentiment lexicons.

中文翻译：

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用于社交媒体情绪分析的带有情绪效价调整的自动印度尼西亚情绪词典策展

一本新颖的印尼情感词典（SentIL——情绪印尼语词典）使用自动管道创建；从创建情感种子词、使用俚语词、表情符号添加新词，以及从给定的字典和情感语料库中添加新词，直到使用标记的情感语料库调整情感值。它首先从词网用英语的情感值映射的BahasaSentiWordNet. 通过将基于字典的方法与单词的同义词和反义词相结合，将基于语料库的方法与单词相似度相结合，从而丰富种子词，这些方法在来自在线市场评论和 Twitter 数据的正面和负面情绪语料库中进行训练。每个词典的效价分数是根据其在语料库中的相对出现重新计算的。我们还添加了一些著名的俚语和表情符号来丰富词典。我们的实验表明，所提出的方法可以提供 3.5 倍的词典数量，在线评论和 Twitter 数据的准确率分别提高了 80.9% 和 95.7%，并且优于其他已发布和可用的印尼情感词典。