本程序目标是训练概念级别的句向量，以分析替换情感词为其对应的概念后是否对构造句向量有影响。具体而言，本程序提供如下功能：
- 提供一个用于训练原始词向量的语料库
- 我们将上述语料库中的词汇，属于ANEW词典中的那部分情感词，按照WordNet同义词典进行替换，得到另一个概念语料库
- 我们用上述两个语料库，使用Doc2vec工具训练两份句项量
- 我们对这两份句向量，对于其中有VA标记的部分，使用简单的线性回归技术，分析他们在预测VA时性能表现是否有差距
进行本实验的动机是，我们观察到无论是使用Word2vec训练词向量，还是使用Doc2vec训练句向量，都面临一个问题，训练出的向量更多的表达的是语义相似性而非情感相似性，如，good和bad虽然表达的是相反的情感倾向，但由于其出现的上下文较相似，因此这两个词向量的比较相似。
为了解决这个问题，我们提出对语料库进行特殊的预处理，将语料库中的情感词替换为其对应的高层概念——synset编号。例如，在WordNet同义词典中，baby和child属于同义词，因此对于语料库中出现baby和child的地方我们用synset_id替换。 在NLTK提供的wordnet工具中，提供了每一个词汇的同义词，然而并没有synset ID。因此，我们将ANEW中同义词替换为ANEW中的词汇。也就是说，我们通过wordnet搜寻所有ANEW中词汇的同义词，然后出现同义词的地方用anew替换。例如： 替换之前的文本如下图：
替换之后的文本如下图：
从上图中可以发现，第一行中，love替换为了passion，第二行中good替换为了serious。这是因为passion和serious为ANEW词典中的词汇，而依据wordnet，love是passion的同义词，good是serious的同义词，见wordnet在线演示网址，其他替换方式类同。更多详细比较，见resource目录中的相关txt文件。
由于在wordnet中，仅仅使用同义词数量较少，如果使用同义词和lemma_names则Synset之间交叉多，如何解决这一问题？我们目前仅仅使用同义词进行替换，而不使用lemma_names，一方面是因为这样会导致Synset数量尽量多，另一方面实现起来较容易。
另一个问题，如何划分词汇到不同的Synset，注意只考虑替换给定词典(如ANEW)中词汇。Map words to synset id, 步骤如下：
- 计算所有ANEW中词汇的Synset
- 替换：对于一组相似同义词，用其中出现在ANEW词典中的词来替换掉语料库中其他所有词，例如，flag一词依据wordnet其同义词有：ease_up, iris, masthead, pin, sag，因此我们将语料库中所有ease_up, iris, masthead, pin, sag词汇用flag一次替换。注意：由于wordnet返回的词语中，包含了短语，如ease_up，用下划线连接起来的，对于这种情况，我们暂不处理。
- 速度优化：数据结构使用dict()
因为使用Doc2vec来训练句向量是非监督式的，但是为了判断句向量质量，我们使用监督式方式，因此语料库分为两类：unlabeled data和labeled data
- Unlabeled data: Stanford twitter corpus from sentiment140
- Labeled data: Vader Twitter corpus
对于用替换和不替换的语料库训练的两份句向量，我们使用其中的labeled data部分，训练一个回归模型，通过分析回归模型的性能，来判断句向量的质量。
我们分别训练了这几种句向量：
- 不做任何预处理
- 有预处理，预处理之后的语料库存储为pickle格式，方便下次直接加载
- 有预处理，并且进行同义词替换，同样处理后的结构用pickle存储
- 词向量维数：300
- windows size: 8
- min count: 10
- epoch: 100
概括：有预处理比没有预处理性能好，同义词是否替换对实验结果影响不大
没有预处理：
| Metrics | MSE | MAE | Pearson_r |
|---|---|---|---|
| ordinary least squares | 1.49259610132 | 0.970785105026 | 0.61255020512241898 |
| Ridge Regression | 1.484225097 | 0.967602444956 | 0.61426306602239911 |
| Bayesian Regression | 1.45401467863 | 0.954559329101 | 0.62166130478403103 |
| SVR | 1.546847209 | 0.980544878277 | 0.59874022849588793 |
| KNN Reg | 2.27236965663 | 1.15422092554 | 0.39873708455401208 |
有预处理：
| Metrics | MSE | MAE | Pearson_r |
|---|---|---|---|
| ordinary least squares | 1.18588698657 | 0.861801766775 | 0.70756349588614698 |
| Ridge Regression | 1.18039015402 | 0.860557769888 | 0.70886360511213908 |
| Bayesian Regression | 1.15727179209 | 0.857067555413 | 0.71607752341210373 |
| SVR | 1.20029817528 | 0.865424229004 | 0.70343288843448581 |
| KNN Reg | 2.01645151621 | 1.10575613528 | 0.46131273504861253 |
预处理+同义词替换：
| Metrics | MSE | MAE | Pearson_r |
|---|---|---|---|
| ordinary least squares | 1.18038914619 | 0.85878648801 | 0.70974246423615184 |
| Ridge Regression | 1.17572872216 | 0.857236881556 | 0.71066619047925106 |
| Bayesian Regression | 1.15633277046 | 0.851493165039 | 0.71596164673843599 |
| SVR | 1.20673631577 | 0.866939930114 | 0.70360523364732996 |
| KNN Reg | 2.14915481032 | 1.13988959412 | 0.44050915767949778 |
没有预处理：
2015-11-11 21:43:32,627 - log_manager - INFO - linear regression Explained variance score: 0.37
2015-11-11 21:43:32,627 - log_manager - INFO - MSE: 1.49259610132, MAE: 0.970785105026, Pearson_r: (0.61255020512241898, 1.1548008591482935e-87), R2: -0.347502977953, Spearman_r: (0.60526409023253303, 4.3034245706995162e-85), sqrt_MSE: 1.2217185033067797
2015-11-11 21:43:32,672 - log_manager - INFO - linear regression Explained variance score: 0.37
2015-11-11 21:43:32,672 - log_manager - INFO - MSE: 1.484225097, MAE: 0.967602444956, Pearson_r: (0.61426306602239911, 2.8070099885082727e-88), R2: -0.376475902221, Spearman_r: (0.60719032867430034, 9.1309479255558613e-86), sqrt_MSE: 1.218287772656195
2015-11-11 21:43:32,989 - log_manager - INFO - linear regression Explained variance score: 0.39
2015-11-11 21:43:32,989 - log_manager - INFO - MSE: 1.45401467863, MAE: 0.954559329101, Pearson_r: (0.62166130478403103, 5.6384710397369307e-91), R2: -0.62660269397, Spearman_r: (0.6147146681658836, 1.9304616996028348e-88), sqrt_MSE: 1.2058253101633096
2015-11-11 21:43:41,873 - log_manager - INFO - linear regression Explained variance score: 0.35
2015-11-11 21:43:41,873 - log_manager - INFO - MSE: 1.546847209, MAE: 0.980544878277, Pearson_r: (0.59874022849588793, 7.5911317184794384e-83), R2: -0.353026259129, Spearman_r: (0.5963620872871338, 4.8577278002144607e-82), sqrt_MSE: 1.2437231239294992
2015-11-11 21:43:45,843 - log_manager - INFO - linear regression Explained variance score: 0.04
2015-11-11 21:43:45,844 - log_manager - INFO - MSE: 2.27236965663, MAE: 1.15422092554, Pearson_r: (0.39873708455401208, 2.1292244447399554e-33), R2: -0.937259773286, Spearman_r: (0.36715551558011367, 3.3360442239675783e-28), sqrt_MSE: 1.5074381103800916
有预处理：
2015-11-11 23:36:00,418 - log_manager - INFO - linear regression Explained variance score: 0.50
2015-11-11 23:36:00,418 - log_manager - INFO - MSE: 1.18588698657, MAE: 0.861801766775, Pearson_r: (0.70756349588614698, 1.6712736443956464e-128), R2: 0.084180966398, Spearman_r: (0.69406124970867411, 1.0008828505969173e-121), sqrt_MSE: 1.0889843830721149
2015-11-11 23:36:00,462 - log_manager - INFO - linear regression Explained variance score: 0.50
2015-11-11 23:36:00,462 - log_manager - INFO - MSE: 1.18039015402, MAE: 0.860557769888, Pearson_r: (0.70886360511213908, 3.5477223379727426e-129), R2: 0.0700383871262, Spearman_r: (0.69542180436110168, 2.1605994405743398e-122), sqrt_MSE: 1.0864576172228462
2015-11-11 23:36:00,808 - log_manager - INFO - linear regression Explained variance score: 0.51
2015-11-11 23:36:00,809 - log_manager - INFO - MSE: 1.15727179209, MAE: 0.857067555413, Pearson_r: (0.71607752341210373, 5.5770753916487455e-133), R2: -0.0465387422234, Spearman_r: (0.70315833348410628, 2.9948846691061593e-126), sqrt_MSE: 1.075765677128298
2015-11-11 23:36:10,740 - log_manager - INFO - linear regression Explained variance score: 0.49
2015-11-11 23:36:10,741 - log_manager - INFO - MSE: 1.20029817528, MAE: 0.865424229004, Pearson_r: (0.70343288843448581, 2.1734917238896289e-126), R2: 0.0637543322379, Spearman_r: (0.692565110336131, 5.3487916786057751e-121), sqrt_MSE: 1.0955812043299649
2015-11-11 23:36:14,885 - log_manager - INFO - linear regression Explained variance score: 0.15
2015-11-11 23:36:14,885 - log_manager - INFO - MSE: 2.01645151621, MAE: 1.10575613528, Pearson_r: (0.46131273504861253, 1.7051785857700428e-45), R2: -1.1185774545, Spearman_r: (0.40850869323860917, 4.0091512487600461e-35), sqrt_MSE: 1.420018139394025
预处理+同义词替换：
2015-11-12 13:37:10,299 - log_manager - INFO - linear regression Explained variance score: 0.50
2015-11-12 13:37:10,299 - log_manager - INFO - MSE: 1.18038914619, MAE: 0.85878648801, Pearson_r: (0.70974246423615184, 1.2383132100315053e-129), R2: 0.121271038151, Spearman_r: (0.70707766629922053, 2.9759233629594366e-128), sqrt_MSE: 1.0864571534069252
2015-11-12 13:37:10,388 - log_manager - INFO - linear regression Explained variance score: 0.50
2015-11-12 13:37:10,388 - log_manager - INFO - MSE: 1.17572872216, MAE: 0.857236881556, Pearson_r: (0.71066619047925106, 4.0787325544405145e-130), R2: 0.107070184954, Spearman_r: (0.70800020526544993, 9.9395544341388438e-129), sqrt_MSE: 1.0843102518001932
2015-11-12 13:37:10,710 - log_manager - INFO - linear regression Explained variance score: 0.51
2015-11-12 13:37:10,710 - log_manager - INFO - MSE: 1.15633277046, MAE: 0.851493165039, Pearson_r: (0.71596164673843599, 6.4333970885345271e-133), R2: -0.0188708366953, Spearman_r: (0.71316382374840792, 1.9808536854345207e-131), sqrt_MSE: 1.0753291451717815
2015-11-12 13:37:20,691 - log_manager - INFO - linear regression Explained variance score: 0.49
2015-11-12 13:37:20,691 - log_manager - INFO - MSE: 1.20673631577, MAE: 0.866939930114, Pearson_r: (0.70360523364732996, 1.7769804325105011e-126), R2: 0.135825053535, Spearman_r: (0.7029631109243577, 3.7607682055022545e-126), sqrt_MSE: 1.0985155054751432
2015-11-12 13:37:24,723 - log_manager - INFO - linear regression Explained variance score: 0.09
2015-11-12 13:37:24,723 - log_manager - INFO - MSE: 2.14915481032, MAE: 1.13988959412, Pearson_r: (0.44050915767949778, 3.4481452164542079e-41), R2: -1.25050146879, Spearman_r: (0.3756021409491484, 1.5459579149703661e-29), sqrt_MSE: 1.4659995942423818
我们发现使用所有数据去训练句向量时，是否进行同义词替换对结果影响甚微，可能的原因是：当数据量较大时，进行同义词替换没有意义，因为只要语料库足够大，Doc2vec是能够自动学习到哪些词语之间是同意关系的。因此，我们猜想如果语料库较小，那么Doc2vec受限于语料库不足，如果进行同义词替换，可以减少词汇量，对实验结果会有更大影响。为此，我们进行下面实验，从语料库中取部分数据。
- Data: Labeled data保持不变，从Unlabeled data中仅仅随机取5万笔资料。
没有预处理：
| Metrics | MSE | MAE | Pearson_r |
|---|---|---|---|
| ordinary least squares | 1.45495316844 | 0.969886812709 | 0.62185136150756593 |
| Ridge Regression | 1.43593950784 | 0.962135267343 | 0.62773204559552531 |
| Bayesian Regression | 1.43182934831 | 0.958339480107 | 0.63134461246281814 |
| SVR | 1.48127438643 | 0.970793953274 | 0.61568264160703678 |
| KNN Reg | 2.35267814609 | 1.18692591371 | 0.37912737339220814 |
有预处理：
| Metrics | MSE | MAE | Pearson_r |
|---|---|---|---|
| ordinary least squares | 1.19929839355 | 0.861187742582 | 0.70389712965786089 |
| Ridge Regression | 1.1825383942 | 0.855270699922 | 0.70804439380547501 |
| Bayesian Regression | 1.16873378781 | 0.85181616883 | 0.7126543721150963 |
| SVR | 1.18849616871 | 0.851067744327 | 0.70718500670800177 |
| KNN Reg | 2.02455131612 | 1.07162000612 | 0.47760173066634337 |
预处理+同义词替换：
| Metrics | MSE | MAE | Pearson_r |
|---|---|---|---|
| ordinary least squares | 1.28709746932 | 0.90445874598 | 0.67931413802365026 |
| Ridge Regression | 1.26489512837 | 0.898439673724 | 0.68408028307796875 |
| Bayesian Regression | 1.25155607736 | 0.896741487218 | 0.68712989692301285 |
| SVR | 1.27324126083 | 0.895949607675 | 0.6839923266210195 |
| KNN Reg | 1.95386840222 | 1.07035624293 | 0.49626371893238819 |
事与愿违，并没有因为同义词替换而提高实验性能，相反，降低了实验性能，但仍然比不预处理结果好。
没有预处理：
2015-11-12 20:22:04,881 - log_manager - INFO - linear regression Explained variance score: 0.39
2015-11-12 20:22:04,881 - log_manager - INFO - MSE: 1.45495316844, MAE: 0.969886812709, Pearson_r: (0.62185136150756593, 4.7964540475266861e-91), R2: -0.461014941945, Spearman_r: (0.61806388307626847, 1.179291378146555e-89), sqrt_MSE: 1.2062143957191902
2015-11-12 20:22:04,956 - log_manager - INFO - linear regression Explained variance score: 0.39
2015-11-12 20:22:04,956 - log_manager - INFO - MSE: 1.43593950784, MAE: 0.962135267343, Pearson_r: (0.62773204559552531, 3.0435616106266763e-93), R2: -0.520213907462, Spearman_r: (0.6246634969145809, 4.3244304165384723e-92), sqrt_MSE: 1.1983069339050247
2015-11-12 20:22:05,303 - log_manager - INFO - linear regression Explained variance score: 0.40
2015-11-12 20:22:05,303 - log_manager - INFO - MSE: 1.43182934831, MAE: 0.958339480107, Pearson_r: (0.63134461246281814, 1.2877284674910151e-94), R2: -0.805980472158, Spearman_r: (0.62930571202078656, 7.7140510454653121e-94), sqrt_MSE: 1.196590718796661
2015-11-12 20:22:13,545 - log_manager - INFO - linear regression Explained variance score: 0.37
2015-11-12 20:22:13,545 - log_manager - INFO - MSE: 1.48127438643, MAE: 0.970793953274, Pearson_r: (0.61568264160703678, 8.6355053351191823e-89), R2: -0.557509643198, Spearman_r: (0.61451623644043074, 2.275787604322019e-88), sqrt_MSE: 1.2170761629518352
2015-11-12 20:22:17,690 - log_manager - INFO - linear regression Explained variance score: 0.01
2015-11-12 20:22:17,691 - log_manager - INFO - MSE: 2.35267814609, MAE: 1.18692591371, Pearson_r: (0.37912737339220814, 4.1750680886225244e-30), R2: -0.77136433227, Spearman_r: (0.36912975990127989, 1.6404405755901991e-28), sqrt_MSE: 1.5338442378851358
有预处理：
2015-11-12 20:28:33,373 - log_manager - INFO - linear regression Explained variance score: 0.49
2015-11-12 20:28:33,373 - log_manager - INFO - MSE: 1.19929839355, MAE: 0.861187742582, Pearson_r: (0.70389712965786089, 1.2629427288714131e-126), R2: 0.0836108949131, Spearman_r: (0.68967173868546194, 1.3282963914078343e-119), sqrt_MSE: 1.0951248301234298
2015-11-12 20:28:33,422 - log_manager - INFO - linear regression Explained variance score: 0.50
2015-11-12 20:28:33,423 - log_manager - INFO - MSE: 1.1825383942, MAE: 0.855270699922, Pearson_r: (0.70804439380547501, 9.4299210825811189e-129), R2: 0.0532009953898, Spearman_r: (0.69422436036427826, 8.3321879672885264e-122), sqrt_MSE: 1.0874458120771116
2015-11-12 20:28:33,752 - log_manager - INFO - linear regression Explained variance score: 0.51
2015-11-12 20:28:33,752 - log_manager - INFO - MSE: 1.16873378781, MAE: 0.85181616883, Pearson_r: (0.7126543721150963, 3.6808703144655336e-131), R2: -0.061716498323, Spearman_r: (0.69977772544170636, 1.5051333637512264e-124), sqrt_MSE: 1.0810799174030379
2015-11-12 20:28:42,029 - log_manager - INFO - linear regression Explained variance score: 0.50
2015-11-12 20:28:42,029 - log_manager - INFO - MSE: 1.18849616871, MAE: 0.851067744327, Pearson_r: (0.70718500670800177, 2.6200141946327201e-128), R2: 0.0804578281165, Spearman_r: (0.69337084376956759, 2.171808932548253e-121), sqrt_MSE: 1.0901817136171912
2015-11-12 20:28:46,313 - log_manager - INFO - linear regression Explained variance score: 0.15
2015-11-12 20:28:46,314 - log_manager - INFO - MSE: 2.02455131612, MAE: 1.07162000612, Pearson_r: (0.47760173066634337, 4.4326330865509996e-49), R2: -0.776328372622, Spearman_r: (0.44704223919277492, 1.6494932501361998e-42), sqrt_MSE: 1.4228672868951024
预处理+同义词替换：
2015-11-12 20:34:17,274 - log_manager - INFO - linear regression Explained variance score: 0.46
2015-11-12 20:34:17,275 - log_manager - INFO - MSE: 1.28709746932, MAE: 0.90445874598, Pearson_r: (0.67931413802365026, 9.6659188365181909e-115), R2: 0.0256952846275, Spearman_r: (0.66652568672326085, 5.2251689822519933e-109), sqrt_MSE: 1.1345031817135056
2015-11-12 20:34:17,322 - log_manager - INFO - linear regression Explained variance score: 0.47
2015-11-12 20:34:17,322 - log_manager - INFO - MSE: 1.26489512837, MAE: 0.898439673724, Pearson_r: (0.68408028307796875, 5.9313786302661068e-117), R2: -0.0169958760582, Spearman_r: (0.67387245912824434, 2.8841259535268039e-112), sqrt_MSE: 1.1246755658260648
2015-11-12 20:34:17,664 - log_manager - INFO - linear regression Explained variance score: 0.47
2015-11-12 20:34:17,664 - log_manager - INFO - MSE: 1.25155607736, MAE: 0.896741487218, Pearson_r: (0.68712989692301285, 2.1644490204062356e-118), R2: -0.157464517273, Spearman_r: (0.67828869632640676, 2.8555653536459443e-114), sqrt_MSE: 1.1187296712628496
2015-11-12 20:34:25,941 - log_manager - INFO - linear regression Explained variance score: 0.46
2015-11-12 20:34:25,941 - log_manager - INFO - MSE: 1.27324126083, MAE: 0.895949607675, Pearson_r: (0.6839923266210195, 6.5217297066186179e-117), R2: 0.0290449522905, Spearman_r: (0.67195466372810908, 2.0872652498958085e-111), sqrt_MSE: 1.128379927518914
2015-11-12 20:34:30,128 - log_manager - INFO - linear regression Explained variance score: 0.18
2015-11-12 20:34:30,129 - log_manager - INFO - MSE: 1.95386840222, MAE: 1.07035624293, Pearson_r: (0.49626371893238819, 1.9700385540358862e-53), R2: -0.71685946257, Spearman_r: (0.4558146100661154, 2.5051703100377535e-44), sqrt_MSE: 1.3978084282966192
由于ANEW中的词汇之间存在同义词现象，为了进一步减少语料库替换后的词汇量，因此我们将属于同义词的ANEW词汇进行合并，然后再替换。例如，ANEW中的词汇ache和hurt是同义词，同时依据wordnet，ache的同义词有hurt，hurt的同义词有ache, damage, detriment, distress, injury, pain, suffer, suffering，因此，将语料库中出现hurt, ache, damage, detriment, distress, injury, pain, suffer, suffering的地方都用hurt替换。
- 发现ANEW中的同义词：
i. 对于ANEW中的词汇A、B，如果A出现在B的同义词集合中，同时B也出现在A的同义词集合中，则认为A、B是同义词，最终发现ANEW中仅有如下4对同义词：
| 单词A | 单词B |
|---|---|
| ache | hurt |
| mangle | mutilate |
| massacre | slaughter |
| mildew | mold |
ii. 对于ANEW中的词汇A、B，如果A出现在B的同义词集合中，或者B出现在A的同义词集合中，则认为A、B是同义词，最终发现ANEW中符合这种规律的词汇较多，详见data/synsets/ANEW_synonym_pairs.md，截取一部分如下：
| 单词A | 单词B |
|---|---|
| agony | torture |
| air | breeze |
| air | tune |
| alert | alive |
| alert | lively |
| alert | merry |
| alert | spanking |
| alone | lonely |
| ... | ... |
iii. 可以发现上述方法中，部分词汇重复出现，如air和breeze，air和tune都是同义词，这是因为通过查询wordnet，air的同义词包含tune和breeze，这三个词都出现在ANEW中。我们对此进一步合并，合并方式是对于上述表格中的任意两行，如果有相同的单词，则合并，形成如下表格：
| cluster_id | 单词集合 |
|---|---|
| cluster_0 | ['abuse', 'corrupt', 'insult', 'pervert'] |
| cluster_1 | ['champion', 'ace', 'champ', 'star'] |
| cluster_2 | ['damage', 'offend', 'wounds', 'troubled', 'nuisance', 'injury', 'trauma', 'ache', 'hurt', 'trouble', 'pain'] |
| cluster_3 | ['addict', 'addicted'] |
| cluster_4 | ['reward', 'honor', 'advantage'] |
| cluster_5 | ['affection', 'heart', 'spirit'] |
| cluster_6 | ['agony', 'torture'] |
| ... | ... |
完整列表见：merge_result.md
- 生成替换表格：
- 对于方案i中的四对同义词，我们用选择一个词汇来替换一组词汇：
| 单词 | 同义词 | 单词 | 同义词 |
|---|---|---|---|
| ache | hurt | hurt | ache damage detriment distress injury pain suffer suffering |
| mangle | maul mutilate | mutilate | mangle |
| massacre | slaughter | slaughter | butcher massacre thrashing |
| mildew | mold | mold | cast determine mildew model shape |
替换方式：
| 替换单词 | 原始单词 |
|---|---|
| hurt | ache damage detriment distress injury pain suffer suffering |
| mangle | maul mutilate |
| slaughter | butcher massacre thrashing |
| mold | cast determine mildew model shape |
可以发现，这个替换方式与之前实验相差不大，因此下略。
- 对于方案iii，替换关系如下：
| 替换词汇 | 原始词汇 |
|---|---|
| abuse | ['bribe', 'corrupt', 'crooked', 'defile', 'diss', 'insult', 'maltreatment', 'mistreat', 'misuse', 'pervert', 'twist'] |
| ace | ['allied_command_europe', 'angiotensin_converting_enzyme', 'asterisk', 'breeze_through', 'champ', 'champion', 'chomp', 'headliner', 'leading', 'one', 'star', 'star_topology', 'supporter'] |
| ache | ['annoyance', 'damage', 'detriment', 'disruptive', 'distress', 'disturb', 'fuss', 'hurt', 'injure', 'injury', 'nuisance', 'offend', 'pain', 'perturb', 'pique', 'price', 'shock', 'suffer', 'suffering', 'transgress', 'trauma', 'trouble', 'trouble_oneself', 'troubled', 'worry', 'wound', 'wounds', 'wrong'] |
| addict | ['addicted'] |
| ... | ... |
完整见replace.md
替换之后的文本如下所示，完整见替换之后（合并）.md：
| 编号 | 句子 |
|---|---|
| 0 | somehow i was blessed with some really amazing friends in my life who beloved me and send encouragement when i am not feeling awesome so lucky |
| 1 | yay another adept phone interview |
| 2 | we were 17 deep last amobarbital_sodium and the bebebebelovedddd was so arch everybody felt it even the band i bebebebelovedddd bebebebelovedddd bebebebelovedddd having adept ppl in my corner |
| 3 | lmao amazing |
| 4 | two words that should die this year sexting and jeggings die die die |
| 5 | today i am grateful for adept food adept wine and adept friends gratitude fb |
| ... | ... |
按照replace.md替换的，可以和替换之前比较。
实验结果：小语料库
| Metrics | MSE | MAE | Pearson_r |
|---|---|---|---|
| ordinary least squares | 1.23029025308 | 0.879368350446 | 0.69592347915028918 |
| Ridge Regression | 1.21413194324 | 0.873161346198 | 0.69931288351577536 |
| Bayesian Regression | 1.20748435487 | 0.869758703768 | 0.70041543666743111 |
| SVR | 1.26198284097 | 0.894285957943 | 0.68751497797359429 |
| KNN Reg | 1.89364786823 | 1.06245751327 | 0.51674357486142264 |
实验结果：完整语料库
| Metrics | MSE | MAE | Pearson_r |
|---|---|---|---|
| ordinary least squares | 1.17914483707 | 0.86150268848 | 0.7088766983834337 |
| Ridge Regression | 1.1757650553 | 0.860516343465 | 0.70982430949063668 |
| Bayesian Regression | 1.1646398601 | 0.857433764353 | 0.71499914436359369 |
| SVR | 1.22098028095 | 0.878793452484 | 0.69758472250904935 |
| KNN Reg | 2.03465043491 | 1.11477867882 | 0.45253552473034059 |
从小语料库的实验结果可以发现，合并ANEW词汇之后再进行替换的方式比没有预处理性能好，比没有进行同义词替换性能稍差，比只进行同义词替换性能稍好。
从完整语料库的实验结果可以发现，合并ANEW词汇之后再进行替换的方式比没有预处理性能好，比没有进行同义词替换性稍差，比只进行同义词替换性能稍差。
增加语料库大小，可以提高实验性能，但是提高幅度不是很大。
1.0.1
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NOTICE: This project is still under developing, providing more useful functions in future.
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