##Background
Classify corpus to do sentiment analysis using emotional dictionary in python.
####Input: A tweet containing several sentences. like: "这手机的画面极好，操作也比较流畅。不过拍照真的太烂了！系统也不好。"
####output: A list containing six fields(Pos, Neg, AvgPos, AvgNeg, StdPos, StdNeg) of the given tweet. like:[3.0, 5.0, 0.75, 1.25, 0.4330127018922193, 1.6393596310755001]
##Files ####sentiment dictionary source sentiment
- 正面评价词语（中文）.txt 中文正面评价词语 3730
- 正面情感词语（中文）.txt 中文正面情感词语 836
- 负面评价词语（中文）.txt 中文负面评价词语 3116
- 负面情感词语（中文）.txt 中文负面情感词语 1254
taiwan
- NTUSD_positive_simplified.txt 2810
- NTUSD_negative_simplified.txt 8276
####BasicEmotionDict
- most.txt 程度级别词语（中文）.txt “极其|extreme / 最|most” 69
- more.txt 程度级别词语（中文）.txt “较|more” 37 add 比较
- very.txt 程度级别词语（中文）.txt “很|very” 42
- ish.txt 程度级别词语（中文）.txt “稍|-ish” 29
- over.txt 程度级别词语（中文）.txt “超|over” 30
- insufficiently.txt 程度级别词语（中文）.txt “欠|insufficiently” 12
##Algorithm Improvements
- 考虑多个程度词的连乘效应。 如："非常不流畅" "流畅"属于posdict, 考虑"流畅"之前的词，"非常"取1×4=4, "不"取4*-1=-4 所以最终虽然"流畅"属于posdict,但此处归于negScore, negScore += 4
- 分句尾的感叹号从该分句正负分值整体考虑，考虑感叹号之前整个分句单词的分值，如果posScore > negScore则posScore += 2, 否则negScore += 2
- to be continued
##Notes Refer to Python 文本挖掘：使用情感词典进行情感分析（算法及程序设计） and Azure-rong。