News Headlines

Abstract

              For this week’s assignment, I conducted research on sentimental analysis for news headlines. Especially, this time I collected and analyzed several news articles from three periods (2000, 2007 and 2015).

Result review

              Thanks to this experiment, I could find a points that is needed to be dealt with. The point is that all of the result for news headlines focused on the keyword “immigrants” tend to “Negative” or “Neutral”. “Positive” articles were quite rare and hardly found them.

Maybe I need to find another analysis method for it to improve my analysis reliabili

*Result (P: Positive / N: Negative / L: Neutral)

2000（BBC）

News headlines	My Reaction	Corenlp	Text Analysis	Result
Immigrant suspects found in lorries.	N	L	L	-
Immigration or extinction.	N	L	L	-
NY police 'deliberately' killed immigrant.	N	N	L	N
'Immigrant' deaths shock papers.	N	N	L	N
Should immigration policy be relaxed?	L	P	L	-
Spain struggles with race riots.	N	L	L	-
German CDU says immigrants must 'conform'.	L	N	L	L
US immigration policy branded 'racist'.	N	N	N	N
Mutual fears behind Spain's race riots.	N	L	L	-

2007（BBC）

News headlines	My Reaction	Corenlp	Text Analysis	Result
Joining the immigrant underclass.	N	L	L	-
The BIG Immigration Debate.	L	L	L	L
Health | Immigrant pregnancies stretch NHS.	L	N	L	L
England | London | Violent immigrants fuelling crime.	N	N	L	N
Hispanic immigrants in the USA.	L	L	L	L
Migrants 'deceived and exploited'.	N	N	L	N
Immigrants re-energise Irish Church.	P	L	L	-
Panorama | Immigration: How we lost count.	N	N	N	N
Immigration to the USA.	L	L	L

 

News headlines	My Reaction	Corenlp	Text Analysis	Result
Immigrant spouses must speak English, court rules.	N	N	L	N
'Blatant' illegal immigrant job offers in Sussex and Kent.	N	N	N	N
Immigration Bill: Landlords 'must evict' illegal immigrants.	N	N	N	N
UK net migration hits record high.	N	P	L	X
The battle over the words used to describe migrants.	N	N	N	N
Illegal immigrants smuggled out of UK and back again.	N	N	L	N
We are beautiful too, say women from immigrant backgrounds.	P	P	L	P
Under the radar with the UK's illegal migrants.	N	L	L	X
EU immigration rules - in 90 seconds.	L	L	L	L

Headline analyses

*Abstract

             

              For this week’s experiment, I did continuously research on the methods of “Sentimental Analysis” for news headlines. Previously, I found two software which can analyze sentences. They are “Corenlp” and “Text analysis (Google sheet’s add-on)”. Therefore, this time, I tried to find the way that increases the credibility of the results of Sentimental analysis using them.

*Method

              As you instructed in the last meeting, I compared the three results of “my reaction”, “Corenlp” and “Text Analysis” to calculate the credible sentimental trend. Then, I also searched for the news headlines which all three criteria regarded as the same category (Positive, Negative and Neutral).

*Result (P: Positive / N: Negative / L: Neutral)

News headlines	My Reaction	Corenlp	Text Analysis	Mean
UK net migration hits record high.	L	P	L	L
Who will be banned under Trump's immigration plan?	L	L	L	L
Eastern European workers in UK pass one million.	N	L	L	L
Australia's immigration minister accuses asylum seekers of lying.	N	N	N	N
Germany's AfD leader wants failed asylum seekers housed on islands.	N	N	N	N
Changing attitudes of a German city.	L	L	N	L
Anti-Islam group storms Anglican church in Australia.	N	L	L	L

*Conclusion

All in all, the result of the experiment was useful in that I could find some possibility that there might be some ways to evaluate the sentimental analysis for news headline using those software. However, I found that there were also many drawbacks to fix in order to get credible information. For example, the reliability of the assessment is still not so strong. It would be necessary for me to learn from the similar themes to make my research method more academically suitable.

Useful Sites for analysis

*Abstract

             

              This time, I scrutinized two research sites which enable me to analyze the sentimental trend among people. One is “Social Searcher” which shows us a lot of useful information about particular keywords. The other is "Google Sheets" that has many remarkable evaluation methods in it. I actually used them, and tried to find out whether it is convenient or inconvenient for my experiment.

---“Social Searcher”---

[Review]

This site can evaluate the detailed sentimental analysis for various SNS community sites including “YouTube”, “Instagram”, “Twitter” and things like that. Also it can evaluate linguistic features like “types of users” and “Keywords density”. However, unfortunately, there are some drawbacks as well. For example, “Social Searcher” has no option to search the result data by the certain date. This means if you want to get information about public reactions one or two years ago, it would be quite difficult because of the deficiency. Given its unstable trait, collecting past data from SNS like “Twitter” might be so complicated. 

---“Google Spread Sheets (Using its add-on “Text Analysis”)”---

[Review]

              Nowadays, there are many computer software which can calculate sentimental analysis. Those divide sentences into three categories “Positive”, “Neutral” and “Negative”. For this week’s report, I happened to find another analysis application called “Google Spread Sheets”. “Google Spread Sheets” is a software which is provided by Google. It is quite similar to “Microsoft Excel” and many add-ons are available for it. “Google Spread Sheets” does not support sentimental analysis. However, its add-on “Text Analysis” enable us to evaluate sentences’ trend. The below is the example of the add-on function.

*For comparison, I attach the result of the Corenlp’s analysis.

Further research on Sentimental analysis tools

*Abstract

             

              This week, I did research on the differences between the news headlines and its contents. The aim of this experiment was to find out what kind of similarity or disparity between them can be observed using the Net-tone analysis.

*Method

              For this research, I picked up five articles about immigration from various media sources and divided them into three categories “Positive”, ”Neutral” and “Negative”. In order to make sure that the result is credible enough, I assumed the attitudes per different sides. Specifically speaking, those groups are the views from Pro-immigrant groups, from Normal groups, from Anti-immigrant groups and Corenlp (only for news headlines) which is a software made by Stanford university scientists to evaluate the texts’ sentimental analysis. Also, I compared the attitudes of news headlines with that of their contents.

*Results

“Why is EU struggling with migrants and asylum?”

http://www.bbc.com/news/world-europe-24583286

Side	Assumed Attitude to Headline	Assumed Attitude to Contents
Pro-IMMG	Negative	Negative (sympathetic)
Normal	Neutral	Negative (sympathetic)
Anti-IMMG	Negative	Negative (worrisome)
Corenlp	Negative	---

“Net migration to UK rises to 333,000 - second highest on record.”

http://www.bbc.com/news/uk-politics-eu-referendum-36382199

Side	Assumed Attitude to Headline	Assumed Attitude to Contents
Pro-IMMG	Positive	Neutral or Negative *This is probably because immigration issue is linked to the Brexit problem in the article.
Normal	Neutral or Negative	Neutral or Negative
Anti-IMMG	Negative	Negative
Corenlp	Positive	---

“EU referendum: Student launches powerful defence of immigration in heated BBC young voters' debate.”

http://www.independent.co.uk/news/uk/politics/eu-referendum-student-launches-powerful-defence-of-immigration-in-heated-bbc-young-voters-debate-a7051321.html

Side	Assumed Attitude to Headline	Assumed Attitude to Contents
Pro-IMMG	Positive	Positive
Normal	Neutral or Positive	Neutral
Anti-IMMG	Negative	Negative (Some of them might feel this report is biased toward liberal views.)
Corenlp	Positive	---

“People are leaving east London because of immigrants.”

http://metro.co.uk/2016/05/14/people-are-leaving-east-london-because-of-immigrants-bbc-documentary-reveals-5882272/

Side	Assumed Attitude to Headline	Assumed Attitude to Contents
Pro-IMMG	Neutral or Negative	Negative (The text explains about immigrants’ problems in the document made by BBC.)
Normal	Neutral or Negative	Neutral or Negative
Anti-IMMG	Negative	Negative (worrisome)
Corenlp	Neutral	---

“The BBC's focus on immigration was a whole day of anti-Brexit propaganda.”

http://www.telegraph.co.uk/news/2016/05/16/the-bbcs-focus-on-immigration-was-a-whole-day-of-anti-brexit-pro/

Side	Assumed Attitude to Headline	Assumed Attitude to Contents
Pro-IMMG	Negative	Negative
Normal	Neutral	Neutral
Anti-IMMG	Positive	Positive
Corenlp	Negative	---

*Conclusion

              All in all, there were interesting outcomes I could get from this attempt. One of them would be the remarkable similarity in the results of the attitude of “Pro-IMMG” and “Corenlp”. In addition, attitude's change between headlines and contents was also quite noteworthy.

CoreNLP

Abstract

             

              During the search for the method to analyze news headlines for Net-tune, I happened to find the quite interesting software called “CoreNLP” which is a toolkit made by many members in Stanford University mainly for the core natural language analysis. (For further information, please refer to this pdf http://nlp.stanford.edu/pubs/StanfordCoreNlp2014.pdf )This Java-based application can evaluate the sentences in many aspects. One of them is Sentimental Analysis. Sentimental Analysis is a language evaluation technics that divides the sentences into three categories. “Positive”, “Negative” and “Neutral”. By using “CoreNLP”, we are able to get the necessary data to obtain the precise Net-tone figures.

Requirements

              As previously mentioned, “CoreNLP” is a Java-based program. Therefore, “Java SE”, which is a Java’s system pack for the Java developers, is needed to be installed to launch this application. In addition, in order to analyze the sentences, which are written on text files, by using this software, it is also needed to put in some command lines on command prompt. This experiment was conducted on Windows, however, the developer team says that it can be run on Mac and Linux as well.

Results for the test

I analyzed some of English sentences including news headlines from BBC using “CoreNLP”. There were two types of evaluations. One is a simple judgement appeared on command prompt. And another one is more detailed data which is output as an xml file.

(To get simple evaluation result, the command line “java -cp "*" -mx5g edu.stanford.nlp.sentiment.SentimentPipeline -file input.txt” is needed to put in. As for the detailed one, “java -cp "*" -Xmx3g edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,parse,pos,sentiment -file input.txt” is used.)