## 3 Method  
### Data Selection and Data Preprocessing  
The data analyzed in this study is from the NASDAQ stock database and the NYSE stock database from 2012 to 2017. According to [PERSON]'s [10] research, the NASDAQ and NYSE stock lists cover 70% of American companies. Although directly gathering data from companies may provide more representative data, this would also be very time-consuming. The data source, however, shows a deviation between the end result of the study and the actual American market. For example, [PERSON]'s work shows that NASDAQ stock covers a remarkably higher percentage of companies in the information technology (IT, 35%) and retailing (21%) sectors than the medical (11%) and biological (7%) [29] sectors. Another potential limitation is that news of certain companies posted on the website is more fully described. It is quite difficult to extract the truly relevant information from such news texts. With a total of 87 NASDAQ stocks and 85 NYSE stocks, the text data includes 408,366 pieces of news from November 11 th, 2012 to November 5 th, 2017. In addition, every company is related to one stock and there is at least one piece of news of a certain company on a given market day. This news includes both brief and full-text news. The closing prices of each stock were regarded as numerical data, with 1200 market days in total and 1200 data points. As the first step in the analysis, Figure 1 shows the path and procedures of data processing. After discarding news not referring to information about business scopes, there were 403,112 pieces of news text left as the total of text data records analyzed directly in this study using statistical computing techniques. The full texts were all preprocessed further for text similarity calculations. In the preprocessing stage, news texts and numeric data were processed separately. For the text, according to [PERSON]'s research [PERSON] (1999), content analysis can be considered in three separate units: the sampling unit, context unit, and recording unit. In this study, the sampling unit has the full-text of all 408,366 pieces of news; the context unit has blocks related to companies' business scopes; and the recording unit covers segments in context units, each of which is supposed to describe a kind of business scope. The example for context units and recording units is shown in Figure 2.  
Citing \"a source that has provided me with reliable information many times in the past\", BGR executive editor [PERSON] reveals a pair of images comparing the changes in the second generation iPhone SE to the first. The key takeaways are:  
An iPhone X inspired facia with no home button  
Face ID has been added, along with the polarising notch  
The headphone jack has been removed  
The angular aluminium chassis has been retained  
What we can't see is whether the 'iPhone SE 2' or 'iPhone X SE' as it is also known (but never call it the 'iPhone SE X') has a dual camera. Such a move would necessitate additional RAM since Apple has always said this is why the iPhone X, iPhone 8 Plus and iPhone 7 Plus have 3 GB rather than the 2 GB featured in the smaller iPhone 8 and iPhone 7. So will Apple really bring a dual camera to an SE edition iPhone? I'm not convinced. Records of units without mentioning or relating to business scopes were removed, and 403,112 items of news were left. After that, every company in this study had at least one piece of news on a market day; some had more. For the consistency and convenience of text similarity calculation, authors merged record units of news which occurred on the same day of a certain company into one text file, which the authors named the \"feature text\". In this way, every company in the 172 companies (87 NASDAQ, 85 NYSE) had one feature text for every market day, 1200 in total. All news was obtained from Yahoo News and Google News, using a web crawler. The structure is shown in Figure 3. The web crawler contains three main parts: the web data acquisition part, the text classification part, and the text storage part.