Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a simple yet efective approach to au tomatically building sentiment lexicons from English sentiment lexicons using publi cly available online machine translation services. The method does not rely on any semanti c resources or bilingual dictionaries, and can be applied to many languages.  propos e to overcome the low coverage problem through putting each English sentiment wor d into diferent contexts to generate diferent phrases, which efectively prompts the m achine translation engine to return diferent translations for the same English sentimen t word. Experiment results on building a Chinese sentiment lexicon (available at https:// github.com/fannix/ChineseSentiment-Lexicon) show that the proposed approach signiic antly improves the coverage of the sentiment lexicon while achieving relatively high pr ecision.
['Xinfan Meng', 'Furu Wei', 'Ge Xu', 'Longkai Zhang', 'Xiaohua Liu', 'Ming Zhou', 'Houfeng Wang']
I am writing a research paper on a idea Lost in Translations? Building Sentiment Lexicons using Context Based Machine Translation
[{"from": "human", "value": "I am writing a research paper on a idea Lost in Translations? Building Sentiment Lexicons using Context Based Machine Translation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a simple yet efective approach to au tomatically building sentiment lexicons from English sentiment lexicons using publi cly available online machine translation services. The method does not rely on any semanti c resources or bilingual dictionaries, and can be applied to many languages.  propos e to overcome the low coverage problem through putting each English sentiment wor d into diferent contexts to generate diferent phrases, which efectively prompts the m achine translation engine to return diferent translations for the same English sentimen t word. Experiment results on building a Chinese sentiment lexicon (available at https:// github.com/fannix/ChineseSentiment-Lexicon) show that the proposed approach signiic antly improves the coverage of the sentiment lexicon while achieving relatively high pr ecision."}]