Dear Mr. Trump, Dear Mr. President ， Please allow me take your liberty to discuss the issue ofthe exchange rate of the RMB to US dollar. 请允许我与您讨论一下人民币 - 美元汇率问题。 During the election campaign and after your victory you havementioned that you would push China to appreciate the value of the RMB. I thinkthis might be big issue in the US-China relation. Frankly speaking I don’t think you can getany positive result. 在这次选举中以及您胜选之后，您都曾经提到过要推动中国对人民币升值。我认为这是中美关系的一个大问题。坦率的说，我认为您难以得到任何正面的结果。 I do not want to ague in length that the US trade deficit isnot because of the exchange rate, in this regard, I just want to remind youthat US has trade deficits with almost every its major trade partner. Forexample, the US have a long deficit record of the many years with Canada, evenno single month without deficit. But you do know there was no any exchangeproblem there. 在这里我不想长篇大论来说明美国的对华贸易赤字是因为汇率而起，我只需提醒您美国几乎与她的每个主要的贸易伙伴都有逆差。比如美国与加拿大有一个很长的逆差记录，而且是年年月月都是逆差，没有一个月例外。但是您知道的，这里毫无汇率的问题。 Nor I’d argue that the reason of the deficits is in thestructure of the US economy. This is also need a lengthy discussion. But in onewords, it is the cost of world hegemony. 同样我不想争论这种逆差的原因是美国的经济结构出了问题。这也需要很长的篇幅。但是可以用一句话来概括：这是霸权的代价。 I’d like suggest we should find out a scientific and objectivecriterion to judge whether the exchange rate is in good shape. 我宁愿建议我们应当寻找一个科学的客观的标准来判断汇率是否正常。 I know quite a large number of economists insist that therate is determined by the market, and refuse to do a research on it. That’slike the ignorant young man who eats when he hungry and eats what he likes. That’s the so called natural way, I wouldlike to say, it is a primitive way. 我知道有很多经济学家，争辩说汇率石油市场决定的，而不肯作进一步的研究。这其实就是一个不懂事的年轻人，说我饿了就吃饭，想吃什么就吃什么。这就是所谓“自然”，其实是原始与无知。 I do think the economist community owns the society such atheory. Economists, as scientists, should be able to explain, and provideguidance to, economy activity. 我的的确确认为经济学界欠社会一个汇率理论。经济学家既然是科学家，就应当奴隶解释经济现象并提供指导。 In this field we only have the PPP,Purchasing Power Parity. But it is good in comparing the living standard indifferent countries, and not so good for exchange rate which is related tointernational trade. Because PPPconsiders the money value from the point of view of end consumers, and anygoods when it comes to the consumer’s hands there must considerable transactioncosts. These transaction costs differ sharply from developing country todeveloped country, so the PPP must overvalue the currency of the developingcountry. 在这个领域我们只有个 PPP ，他虽然对于比较不同国家的生活水平有用，但是却不适于解释汇率。因为 PPP 是从最终消费者的角度去考虑货币的购买力。而任何商品要达到最终消费者的手中必然要经过许多交易过程，这交易成本在发达果胶和不发到国家之间有很大的差异，这就使得 PPP 高估了发展中国家的货币（相对于国际贸易而言）。 I have put forward a formula of exchange rate, and discussedit with many established scholars including quite a number of the Nobel laureates,for example the out spoken Dr. Paul Krugman. They did not have a single word ofobjection. 我曾经提出一个汇率的公式，并且与许多著名的经济学家讨论过，其中有好几位是诺贝尔奖得主，例如就有大嘴巴的保罗·克鲁格曼。他们都没有提出任何反对意见。 I believe, if someone of them had a good reason to say “NO”,he or she would have not hesitated to teach a lesson, because teaching is theirnature. 我相信，假如他们中间哪一个认为有站得住脚的理由反对我的意见，他就会毫不犹豫的给我上一课，因为这是学者的天性。 Of course no one of them likes to take this hot potato. Itis too hot an issue in the States. However if you, the President of the UnitedStates ask them to provide a theoretic opinion they would do their job. 当然他们中间也没有谁愿意借这个烫手的山芋，在美国这个问题是在是太烫手了。但是，过您，美国总统，要他们提供学术上的意见，他们就会做他们该做的工作。 My idea is like this: Take as much as possible the products the concernedcountries can produce with similar standard, use the two moneys calculate thetotal value of the big sum respectively, then the ratio of the two sum valueswould be the expectation value of exchange rate. The real exchange rate should fluctuatearound this expected value. 我的这个想法是： 选取两国都能生产的产品的总量，分别用两国的货币来计算这个总量的价值。这两个价值的比值，就是汇率的期望值。 Put is more exactly, the expectation value of the exchangerate R, 写成数学公式就是 R1-2=Sum2/Sum1; Sum1 is total value of the selected goods calculated withmoney of the first country and the price in the first country, Sum 就是选出的东西用第一个国家的货币计算的价值。 Sum1= ΣViP1i, Sum2= ΣViP2i Where, index i runs from 1 to N, for the N kinds ofproducts, Vi, the total volume of i-thproduct both country produced in the specific period; p1i, the price of i-th product in country1; In the case of USD to RMB, it is the price in USand in USD, while P2 is the price in China in RMB . So Sum1 is the total value inUSD of the chosen products that the two countries produced. Similarly Sum2 isthe value of this lump sum in RMB. 这里下标 i 从 1 到 N ， N 是选出的产品的种类数目； Vi ，是第 i 种产品的数量，两国在一个特定的期间生产的的总数； P1i ，第 i 中产品在第一个国家的价格。在我们讨论的美元 兑人民币的场合，就是买美国的以美元计价的价格， P2 就是在中国的以人民币计价的价格。 Had the PPP evaluated from production side and take intoconsideration of those products that the two countries can produced with thesame quality standard, it would become to mine. 假如 PPP 的计算是从生产的角度而且考虑大量的两国都能生产的商品，那么它就与我的概念一致了。 Here the words “ from production side” means use thefactory gate price, while PPP uses the retailer price to the final consumer.However since the exchange rate is for international trade, maybe some otherprice close to this trade more suitable. 这里“从生产角度”意味着使用出厂价，然而 PPP 使用的是对最终消费者的零售价。 What kind of product, and what kind of price should be taken,are subject to academic discussion. There are still rooms of dispute, however it shifted from sentimental quarrelto reasonable discussion, and mayproduce positive result. 应当选用那些产品，应当使用什么价格，这里还有很大的讨论的余地。然而这样我们就把会的争论从感情冲动争吵印象理性的讨论，因而会产生有益的结果。 I appreciated your idea of “achieve a stable, peaceful worldwith less conflict and more common ground” 我很欣赏您的外交理念，“实现世界的和平与稳定，少一些冲突，多一些共同的基础”。 In doing so, I think you would agree, maintaining sound workrelation with your partner of China is of vital significance. 为此，我相信您会同意，与您的中国伙伴保持良好的工作关系非常重要。 Thank for listening. Sincerely yours, Yankun Chi
Final update before election: Net sentiment last 24 hours: Trump +7 ; Clinton -9. The last day analysis of social media. Buzz: So contrary to the popular belief, Trump actually is leading in social media just before the election day. Compare the above with last month ups and downs to put it in larger context: Last 3 month sentiment: Trump -11; Clinton -18. Buzz for Trump never fails: Trump's Word Clouds: Clinton's Word Clouds: Trump 3-month summary: Clinton 3-month summary: Ethnicity: RW: 伟哥的东西，好是好，就是没有体现美国的选人制度 Xin: 主要是白人黑人和亚裔人数比例并没有代表实际的选民百分比。 RW: 理论上讲，只要有一方得到所有选票的23%, 他或她就可能当选 【大数据跟踪美大选每日更新，希拉里成功反击，拉川普下水】 【社煤挖掘：大数据告诉我们，希拉里选情告急】 Trump sucks in social media big data in Spanish Did Trump’s Gettysburg speech enable the support rate to soar as claimed? 【社煤挖掘：川普的葛底斯堡演讲使支持率飙升了吗？】 【社煤挖掘：为什么要选ta而不是ta做总统？】 Big data mining shows clear social rating decline of Trump last month Clinton, 5 years ago. How time flies … 【社媒挖掘：川大叔喜大妈谁长出了总统样？】 【川普和希拉里的幽默竞赛】 【大数据舆情挖掘：希拉里川普最近一个月的形象消长】 欧阳峰： 论保守派该投票克林顿 【立委科普：自动民调】 【关于舆情挖掘】 《朝华午拾》总目录 【关于立委NLP的《关于系列》】 【置顶：立委NLP博文一览】 【 立委NLP频道 】
As promised, let us get down to the business of big data mining of public opinions and sentiments from Spanish social media on the US election campaign. We know that in the automated mining of public opinions and sentiments for Trump and Clinton we did before , Spanish-Americans are severely under-represented, with only 8% Hispanic posters in comparison with their 16% in population according to 2010 census (widely believed to be more than 16% today), perhaps because of language and/or cultural barriers. So we decide to use our multilingual mining tools to do a similar automated survey from Spanish Social Media to complement our earlier studies . This is Trump as represented in Spanish social media for the last 30 days (09/29-10/29), the key is his social rating as reflected by his net sentiment -33% (in comparison with his rating of -9% in English social media for the same period): way below the freezing point, it really sucks, as also illustrated by the concentration of negative Spanish expressions (red-font) in his word cloud visualization. By the net sentiment -33%, it corresponds to 242,672 negative mentions vs. 121,584 positive mentions, as shown below. In other words, negative comments are about twice as much as positive comments on Trump in Spanish social media in the last 30 days. This is the buzz in the last 30 days for Trump: mentions and potential impressions (eye balls): millions of data points and indeed a very hot topic in the social media. This is the BPI (Brand Passion Index) graph for directly comparing Trump and Clinton for their social ratings in the Spanish social media in the last 30 days: As seen, there is simply no comparison: to refresh our memory, let us contrast it with the BPI comparison in the English social media : Earlier in one of my election campaign mining posts on Chinese data , I said, if Chinese only were to vote, Trump would fail horribly, as shown by the big margin in the leading position of Clinton over Trump: This is even more true based on social media big data from Spanish. This is the comparison trends of passion intensity between Trump and Clinton: The visualization by weeks of the same passion intensity data, instead of by days, show even more clearly that people are very passionate about both candidates in the Spanish social media discussions, the intensity of sentiment expressed for Clinton are slightly higher than for Trump: This is the trends graph for their respective net sentiment, showing their social images in Spanish-speaking communities: We already know that there is simply no comparison: in this 30-day duration, even when Clinton dropped to its lowest point (close to zero) on Oct 9th, she was still way ahead of Trump whose net sentiment at the time was -40%. In any other time segments, we see an even bigger margin (as big as 40 to 80 points in gap) between the two. Clinton has consistently been leading. In terms of buzz, Trump generates more noise (mentions) than Clinton consistently, although the gap is not as large as that in English social media: This is the geo graph, so the social data come from mostly the US and Mexico, some from other Latin America countries and Spain: Since only the Mexicans in the US may have the voting power, we should exclude media from outside the US to have a clearer picture of how the Spanish-speaking voters may have an impact on this election. Before we do that filtering, we note the fact that Trump sucks in the minds of Mexican people, which is no surprise at all given his irresponsible comments about the Mexican people. Our social media tool is equipped with geo-filtering capabilities: you can add a geo-fence to a topic to retrieve all social media posts authored from within a fenced location. This allows you to analyze location-based content irrespective of post text. That is exactly what we need in order to do a study for Spanish-speaking communities in the US who are likely to be voters, excluding those media from Mexico or other Spanish-speaking countries. communities in the US who are likely to be voters, excluding those media from Mexico or other countries. This is also needed when we need to do study for those critical swing states to see the true pictures of the likelihood of the public sentiments and opinions in those states that will decide the destiny of the candidates and the future of the US (stay tuned, swing states social media mining will come shortly thanks to our fully automated mining system based on natural language deep parsing). Now I have excluded Spanish data from outside America, it turned out that the social ratings are roughly the same as before: the reduction of the data does not change the general public opinions from Spanish communities, US or beyond US., US or beyond US. This is US only Spanish social media: This is summary of Trump for Spanish data within US: It is clear that Trump's image truly sucks in the Spanish-speaking communities in the US, communities in the US, which is no surprise and so natural and evident that we simply just confirm and verify that with big data and high-tech now. These are sentiment drivers (i.e. pros and cons as well as emotion expressions) of Trump : We might need Google Translate to interpret them but the color coding remains universal: red is for negative comments and green is positive. More red than green means a poor image or social rating. In contrast, the Clinton's word clouds involve way more green than red: showing her support rate remains high in the Spanish-speaking communities of the US. It looks like that the emotional sentiments for Clinton are not as good as Clinton's sentiment drivers for her pros and cons. Sources of this study: Domains of this study: Did Trump's Gettysburg speech enable the support rate to soar as claimed? Big data mining shows clear social rating decline of Trump last month Clinton, 5 years ago. How time flies … Automated Suevey Dr Li’s NLP Blog in English
Last few days have seen tons of reports on Trump's Gettysburg speech and its impact on his support rate, which is claimed by some of his campaign media to soar due to this powerful speech. We would love to verify this and uncover the true picture based on big data mining from the social media. First, here is one link on his speech: DONALD J. TRUMP DELIVERS GROUNDBREAKING CONTRACT FOR THE AMERICAN VOTER IN GETTYSBURG . (The most widely circulated related post in Chinese social media seems to be this: Trump's heavyweight speech enables the soaring of the support rate and possible stock market crash ). Believed to be a historical speech in his last dash in the campaign, Trump basically said: I am willing to have a contract with the American people on reforming the politics and making America great again, with this plan outline of my administration in the time frame I promised when I am in office, I will make things happen, believe me. Trump made the speech on the 22nd this month, in order to mine true public opinions of the speech impact, we can investigate the data around 22nd for the social media automated data analysis. We believe that automated polling based on big data and language understanding technology is much more revealing and dependable than the traditional manual polls, with phone calls to something like 500 to 1,000 people. The latter is laughably lacking sufficient data to be trustworthy. What does the above trend graph tell us? 1 Trump in this time interval was indeed on the rise. The soaring claim this time does not entirely come out of nowhere, but, there is a big BUT. 2. BUT, a careful look at the public opinions represented by net sentiment (a measure reflecting the ratio of positive mentions over negative mentions in social media) shows that Trump has basically stayed below the freezing point (i.e. more negative than positive) in this time interval, with only a brief rise above the zero point near the 22nd speech, and soon went down underwater again. 3. The soaring claim cannot withstand scrutiny at all as soaring implies a sharp rise of support after the speech event in comparison with before, which is not the case. 4. The fact is, Uncle Trump's social media image dropped to the bottom on the 18th (with net sentiment of -20%) of this month. From 18th to 22nd when he delivered the speech, his net sentiment was steadily on rise from -20% to 0), but from 22nd to 25th, it no longer went up, but fell back down, so there is no ground for the claim of support soaring as an effect of his speech, not at all. 5. Although not soaring, Uncle Trump's speech did not lead to sharp drop either, in terms of the buzz generated, this speech can be said to be fairly well delivered in his performance. After the speech, the net sentiment of public opinions slightly dropped, basically maintaining the fundamentals close to zero. 6. The above big data investigation shows that the media campaign can be very misleading against the objective evidence and real life data. This is all propaganda, which cannot be trusted at its face value: from so-called support rate soared to possible stock market crash. Basically nonsense or noise of campaign, and it cannot be taken seriously. The following figure is a summary of the surveyed interval: As seen, the average public opinion net-sentiment for this interval is -9%, with positive rating consisting of 2.7 million mentions, and negative rating of 3.2 million mentions. How do we interpret -9% as an indicator of public opinions and sentiments? According to our previous numerous automated surveys of political figures, this is certainly not a good public opinion rating, but not particularly bad either as we have seen worse. Basically, -9% is under the average line among politicians reflecting the public image in people's minds in the social media. Nevertheless, compared with Trump's own public ratings before, there is a recorded 13 points jump in this interval, which is pretty good for him and his campaign. But the progress is clearly not the effect of his speech. This is the social media statistics on the data sources of this investigation: In terms of the ratio, Twitter ranks no 1, it is the most dynamic social media on politics for sure, with the largest amount of tweets generated every minute. Among a total of 34.5 million mentions on Trump, Twitter accounted for 23.9 million. In comparison, Facebook has 1.7 million mentions. Well, let's zoom in on the last 30 days instead of only the days around the speech, to provide a bigger background for uncovering the overall trends of this political fight in the 2016 US presidential campaign between Trump and Clinton. The 30 days range from 9/28-10/28, during which the two lines in the comparison trends chart show the contrast of Trump and Clinton in their respective daily ups and downs of net sentiment (reflecting their social rating trends). The general impression is that the fight seems to be fairly tight. Both are so scandal-ridden, both are tough and belligerent. And both are fairly poor in social ratings. The trends might look a bit clearer if we visualize the trends data by weeks instead of by day: No matter how much I dislike Trump, and regardless of my dislike of Clinton whom I have decided to vote anyway in order to make sure the annoying Trump is out of the race, as a data scientist , I have to rely on data which says that Hillary's recent situation is not too optimistic: Trump actually at times went a little ahead of Clinton (a troubling fact to recognize and see). The graph above shows a comparison of the mentions (buzz, so to speak). In terms of buzz, Trump is a natural topic-king, having generated most noise and comments, good or bad. Clinton is no comparison in this regard. The above is a comparison of public opinion passion intensity: like/love or dislike/hate? The passion intensity for Trump is really high, showing that he has some crazy fans and/or deep haters in the people. Hillary Clinton has been controversial also and it is not rare that we come across people with very intensified sentiments towards her too. But still, Trump is sort of political anomaly, and he is more likely to cause fanaticism or controversy than his opponent Hillary. In his recent Gettysburg speech, Trump highlighted the so-called danger of the election being manipulated. He clearly exaggerated the procedure risks, more than past candidates in history using the same election protocol and mechanism. By doing so, he paved the way for future non-recognition of the election results. He was even fooling the entire nation by saying publicly nonsense like he would totally accept the election results if he wins: this is not humor or sense of humor, it depicts a dangerous political figure with ambition unchecked. A very troubling sign and fairly dirty political tricks or fire he is playing with now, to my mind. Now the situation is, if Clinton has a substantial lead to beat him by a large margin, this old Uncle Trump would have no excuse or room for instigating incidents after the election. But if it is closer to see-saw, which is not unlikely given the trends analysis we have shown above, then our country might be in some trouble: Uncle Trump and his die-hard fans most certainly will make some trouble. Given the seriousness of this situation and pressing risks of political turmoil possibly to follow, we now see quite some people, including some conservative minds, begin to call for the election of Hillary for the sake of preventing Trump from possible trouble making. I am one with that mind-set too, given that I do not like Hillary either. If not for Trump, in ordinary elections like this when I do not like candidates of both major parties, I would most likely vote for a third party, or abstain from voting, but this election is different, it is too dangerous as it stands. It is like a time bomb hidden somewhere in the Trump's house, totally unpredictable. In order to prevent him from spilling, it is safer to vote for Clinton. in comparison with my earlier automated sentiment analysi blogged about a week ago ( Big data mining shows clear social rating decline of Trump last month )，this updated, more recent BPI brand comparison chart seems to be more see-saw: Clinton's recent campaign seems to be stuck somewhere. Over the last 30 days, Clinton's net sentiment rating is -17%, while Trump's is -19%. Clinton is only slightly ahead of Trump. Fortunately, Trump's speech did not really reverse the gap between the two, which is seen fairly clearly from the following historical trends represented by three different circles in brand comparison (the darker circle represents more recent data): the general trends of Clinton are still there: it started lagging behind and went better and now is a bit stuck, but still leading. Yes, Clinton's most recent campaign activities are not making significant progress, despite more resources put to use as shown by bigger darker circle in the graph. Among the three circles of Clinton, we can see that the smallest and lightest circle stands for the first 10 days of data in the past 30 days, starting obviously behind Trump. The last two circles are data of the last 20 days, seemingly in situ, although the circle becomes larger, indicating more campaign input and more buzz generated. But the benefits are not so obvious. On the other side, Trump's trends show a zigzag, with the overall trends actual declining in the past 30 days. The middle ten days, there was a clear rise in his social rating, but the last ten days have been going down back. Look at Trump's 30-day social cloud of Word Cloud for pros and cons and Word Cloud for emotions: Let us have a look at Trump's 30-day social media sentiment word clouds, the first is more about commenting on his pros and cons, and the second is more direct and emotional expressions on him: One friend took a glance at the red font expression fuck, and asked: what afre subjects and objects of fuck here?The subject generally does not appear, the default is a general network In fact, the subject generally does not appear in the social posts, by default it is the poster himself, reflecting part of the general public, the object of fuck is, of course, Trump, for otherwise our deep linguistics based system will not count it as a negative mention of trump reflected in the graph. Let us show some random samples side by side of the graph: My goodness, the fuck mentions accounted for 5% of the emotional data, the poor old Uncle Trump were fucked 40 million times in social media within only one-month duration, showing how this guy is hated by some of the people whom he is supposed to represent and govern if he takes office. See how they actually express their strong dislike of trump: fucking moron fucking idiot asshole shithead you name it, to the point even some Republicans also curse him like crazy: Trump is a fucking idiot. Thank you for ruining the Republican Party you shithead. Looking at the following figure of popular media, it seems that the most widely circulated political posts in social media involve quite some political video works: The domains figure below shows that the Tumblr posts on politics contribute more than Facebook: In terms of demographics background of social media posters, there is a fair balance between make and female: male 52% female 48% (in contrast to Chinese social media where only 25% females are posting political comments on US presidential campaign ). The figure below shows the ethnic background of the posters, with 70% Caucasians, 13% African Americans, 8% Hispanic and 6% Asians. It looks like that the Hispanic Americans and Asian Americans are under-represented in the English social media in comparison with their due population ratios, as a result, this study may have missed some of their voice (but we have another similar study using Chinese social media , which shows a clear and big lead of Clinton over Trump ; given time, we should do another automated survey using our multilingual engine for Spanish social media. Another suggestion from friends is to do a similar study on swing states because after all these are the key states that will decide the outcome of this election, we can filter the data by locations where posts are from to simulate that study). There might be a language or cultural reasons for this under-representation. This last table involves a bit of fun facts of the investigation. In social media, people tend to talk most about the campaign, on the Wednesday and Sunday evenings, with 9 o'clock as the peak, for example, on the topic of Trump, nine o'clock on Sunday evening generated 1,357,766 messages within one hour. No wonder there is no shortage of big data from social media on politics. It is all about big data. In contrast, with the traditional manual poll, no matter how sampling is done, the limitation in the number of data points is so challenging: with typically 500 to 1000 phone calls, how can we trust that the poll represents the public opinions of 200 million voters? They are laughably too sparse in data. Of course, in the pre-big-data age, there were simply no alternatives to collect public opinion in a timely manner with limited budgets. This is the beauty of Automatic Survey , which is bound to ourperform the manual survey and become the mainstream of polls. The following figure is the most influential followers authors: Authors with most followers are: Most mentioned authors are listed below： Tell me when in history did we ever have this much data and info, with this powerful data mining capabilities of fully sutomated mining of public opinions and sentiments at scale? Big data mining shows clear social rating decline of Trump last month Clinton, 5 years ago. How time flies … Automated Suevey Dr Li’s NLP Blog in English
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标签: Trump
