Some further analysis and findings:
1. The existing data are not very large (400k mentions a year), but the results make sense with decent data quality
2. From geos stats, we know most data on Walmart come from China (dark color) instead of overseas sources
3. From domains stats, the data actually include data from Sina Weibo (weibo.com) and Tencent Weibo (t.qq.com) although the data flow from these two important Microblog sources is not stable at this point. Also the domains stats show that the major domains are all from China. I know that Walmart is a very influential brand in China and has many stores in cities of China.
4. The net sentiment 48% is fairly high, which is reflected in the emotions stats （data quality very good）: big green fonts emotional terms include 放心 (piece of mind)，喜欢 (like)，乐 (happy)，支持/推 (support)，很好 (very good), 不错(not bad)，成功 (success) etc. The negative emotional words (in small red font) are not many, including 差劲 (bad)，抱怨 (complain)，不喜欢 (dislike)，垃圾 (garbage)，很一般 (very so-so: meaning not as good as expected).
5. In the proscons word cloud, the likes include money-saving （省钱/便宜）and first-class service（服务一流）; more interesting insights come from the dislikes, including (1) fake beef (using fox meat 狐狸肉事件); (2) recall (召回some product?); (3) cheating（欺诈）; (4) scandal（丑闻） etc.
6. In order to drill down to see what negative incidents led to the above dislikes, the Walmart_con_sample shows some related sound bites which look like negative news on some incidents: 1st sound bite reports CCTV news on Walmart’s fake alcohol and fake meat (using fox meat) incidents; 2nd sound bite reports using fox meat to fake beef and donkey meat and using chicken to fake beef in the sold burgers at its Sam’s Club; the third sound bite reports three incidents of Walmart at different times and its apologies, including using cheap frozen meat to fake organic green food; using cheap fox meat to fake beef; and its lack of quality control in importing low quality products for sale, having issued 200 permits within 7 years for disqualified products to be on shelf.
7. Note that the above sound bites are selectively collected to show that our system can indeed capture detailed negative incidents of the brand in the media. When I drill down, there are quite some duplicates in our sound bites (one bad news gets re-posted everywhere); another thing is that the negative comments are not mainly from social media users, but from news (state-run news which get posted in social media too).
8. Unlike the overwhelming positive terms in emotions word cloud and the summary, the behavior word cloud shows more or bigger negative behavior terms than the positive terms. This is understandable because of the heavily reported incidents as shown above in the sample sound bites. Eye-catching negative behavior terms include “revealed”（被曝）, “take to court”/”being sued”（告上法庭）; “closed”（关闭）; “have to take off shelf” （下架）etc.
9. From the above negative behavior terms, I drilled down to see more details in the sample sound bites below, which is similar to the sample discussed in 6. These two sound bites both come from negative news of Walmart, which originated from traditional news and got spread all over Internet.
不仅如此，最近还听说，由于中美相互指责对方利用网络偷窃情报，IT 业关系恶化，以至于谷歌和苹果等公司在中国遭到进一步打压，连做学问的信息利器 Google Scholar 都被封杀了。造孽啊，城门失火，殃及池鱼。