Discover Top Posts Tagged with #netlytics

Netlytic Analysis of #VirtualReality

The definition of virtual reality comes from the definitions from both ‘virtual’ and ‘reality’. The meaning of virtual is near and reality is what we experience as human beings. So, it basically means ‘’near-reality’’. To define virtual reality in technical terms, it’s a three-dimensional computer-generated environment which can be explored and interacted with by a person or multiple people.

Now, a combination of virtual reality and artificial intelligence (intelligence demonstrated by machines) will unlock a future with safe and competent intelligent machines, able to learn exponentially through self-training and intelligent, realistic simulations. You can begin to see how the mingling of academic people and technologies might combine Virtual Reality and Machine Learning to create a force more disruptive than either alone.

In order to understand how people, respond to a technological future within the education, I analyzed two hashtags #virtualreality and #education on Twitter by using the data analyzing tools called Netlytics and Gephi. Because Twitter is a platform that uses a lot of hashtags when posting something, I chose for Twitter to analyze data about these two hashtags.

Text Analysis

I did the text analysis of these two hashtags, using Netlytics as mentioned before. You can find all the graphs and charts down below with explanation added beneath it.

Figure 1. Word Cloud.

The Word Cloud shows that #virtualreality is the biggest popular topic on Twitter. #virtualreality has been mentioned 1584 times in people their tweets. The three most popular hashtags are: - #virtualreality (frequency: 1584), - #VR (frequency: 1096), - and #education (frequency: 597).

This shows that virtual reality is quite related to the topic of education and artificial intelligence since it happens more and more in the classrooms that the teachers work with virtual reality to educate the students.

Figure 2. Words over Time.

This visual shows the Words over Time, the two biggest bars in the graphic is as you can see: Virtual Reality (purple bar) and Education (mint-y bar). There are both quite equal in size. These two were the most used words over time. If you look at the time part, it these two hashtags has been used constantly. Where education lowers in use, virtual reality is still being used a lot. After these two, the hashtags #technology is the 3rd biggest word that has been used quite a lot In between too.

Figure 3. Manual Categories.

This image shows that the tweets that include the hashtags #education and #virtualreality mostly belong to which category. As you can see, the biggest category is ‘’feelings (good)’’. This means that the tweets about #education and #virtualreality combined mostly belong to the category of ‘’good feelings’’ which is 80% of all the categories. The second and third best categories are Time (43) and Appearance (24).

Figure 4. Top Ten Posters Mentioned in Messages.

According to the Report in the Netlytic tool, it shows that ‘kuriharan’ is on top of the top 10 Posters mentioned in messages (#mentions: 20). Following with discoverhaptics (#mentions: 9), allybarrow (#mentions: 9), silviadellai1 (#mentions: 6), gfxcoach (#mentions: 6), beatsaber (#mentions: 6), certesnews (#mentions: 6), sgh_rts (#mentions: 6), technative (#mentions: 6), and haroldsinnott (#mentions: 6).

The user who was mentioned the most, Kuriharan, is the President of Tokyo Chapter at Government Blockchain Association in Tokyo, Japan. The second user, Discoverhaptics, is a twitter profile of a company who is all about haptic technology. They share their passion for haptics and a whole lot of information about haptics. This shows that the organization is quite in the virtual reality world and into making the interaction between a human being and a machine more ‘’humane’’. Which also has a BIG influence on the combination of virtual reality and education.

#morsal #netlytics #virtual reality

Netlytic Analysis of #IOT

The internet of things is the concept of basically connecting any device with an on and off switch to the Internet (and/or to each other). This includes almost everything that is electric. So basically, the IoT is a giant network of connected “things” (which also includes people) (Morgan, 2014). In this research, I’m planning to connect the famous internet of things to education because in this age every educational institution uses the internet of things and I wanted to see how they correlate with each other.

In addition to this, I did several analysis’ on Twitter and Instagram to see how people respond with using the Internet of things within the sector of education. On hashtagify.me, results show that #iot was only used 79 times on Instagram and for Twitter, #internetofthings and #iot was used 735 and 762 times. As a result, I made the decision that Twitter is most suitable as there was way more conversation on that platform. Also, I first did a text analysis without education and only with the hashtag internet of things. After that, I included the hashtag education and the results were quite interesting.

Text Analysis

Figure 1.1. Word cloud Netlytics

I started the text analysis with using the tool Netlytics and creating a word cloud within it. The word cloud shows that the hashtags internetofthings (735) and iot (762) are a very popular topic within Twitter. It’s very closely related to our main topic, namely by the way the hashtags ai (233) and artificial intelligence (205) are mentioned loads of times connected to my particular hashtags. #bigdata (242), #blockchain (196), #machinelearning (164) and #socialinfluence (150) were also mentioned a lot on the platform and this fits well towards our topic. The one thing that was interesting to me were the hashtags #smartcity (66) and #businessintelligence (64) as these are also related in a way to education as the internet can make you smarter and help you with business or making smart cities. It is interesting that these things were mentioned on Twitter. This whole graph indicates that people are interested in the internet of things and the technology that comes with it such as AI, deep learning, block chains etc.

Figure 1.2 Top 10 mentioned hashtags in wordcloud

Here are the top 10 most mentioned hashtags related to #internetofthings. What I found interesting about this chart is that #socialinfluence is included. I think this is because on the internet there are a lot of influencial things right now. Can be people, but also chatbots and other AI related things. And this part includes again; education. With the internet of things there are loads of people being influenced, some in a good way and some in bad ways. But in the end we learn something out of it instead.

I was interested how related education actually is to internet of things and therefore I did another dataset on netlytics but included the #education into it. Then, I did the wordcloud again, and here is the result:

Figure 1.3 Wordcloud with #education included

From this wordcloud we can see that there are a lot of different hashtags therefore interesting ones such as #connectingpeople (5), #healthcare (8) and #topinfluence (10). On the other hand, the wordcloud is way smaller but education and internet of things are quite related as they have 27 of the same tweets mentioned.

Figure 1.4 Words over time

In this graph, you can see that in the word cloud the most important words were the internet of things, iot and education. Also, the word “tech” is a word that has been used a lot in relation to the internet of things. Together with a lot of Twitter users, it had a high peak during the 17th of May. It is interesting that around that time many more people were suddenly intrigued by the word technology. On the other side, #internetofthings and #education are very consistent during the analysis, they are both interesting at the same level, which indicates that people were interested in both of the hashtags during this time.

#marike #netlytics #iot

Netlytic Analysis of #DeepLearning

TEXT ANALYSIS

In figure 1.1 we see the most popular hashtags used in combination with #deeplearning, which is the hashtag with the largest data [1011]. The second largest hashtag is #machinelearning [798], followed by #ai [856], #bigdata [622], #iot [464], #nlp [284], #artificialintelligence [283] and #robots [281].

WORDCLOUD

Figure 1.1

Figure 1.2

WORDS OVER TIME

Figure 1.3 shows the use of the earlier mentioned hashtags over a period of time. As we can see, #deeplearning is together with #machinelearning the most popular hashtag in comparison to other hashtags (or usernames) at that time. The hashtag #deeplearning seems to have two slightly lows, right after two peak points, but has without a doubt the largest data set. Looking at posts connected to these words, I tried to find something related to either education or finance. I came across the posts you can see below [figure 1.3.1]. This post by @JohnTweepy mentions the hashtags #deeplearning, but also #fintech and #insurtech

Figure 1.3 Words over time Figure 1.3.1 Post [Tweet] by @JohnTweepy

CATEGORIES When you take a look at figure 1.4, it is obvious that size [81] and shape [72] are the two largest categories. Adjectives used in the size category are; big [65], massive [9], small [3], short [3] and huge [1]. In the shape category, words that are being mentioned are; deep [52], high [17] and straight [3]. Another category which came up quite often is appearance [36], with the adjectives light [25], dark [8] and long [3]. Moreover, feelings (good) [33], the words that were mentioned here, are nice [13], great [11], proud [3], kind [2], happy [2] and good [2]. Lastly, we have the category quantity [29], with only two adjectives; light [25] and many [4].

Figure 1.4 Categories

#judith #deep learning #netlytics

Netlytic Analysis of #FinTech

FinTech is a combination of the words ‘Finance’ and ‘Technology’. Artificial Intelligence(AI) is now actively being used in this fintech field. Recently, AI has helped the technological advancements in the financial industry, such as using facial recognition to log in to financial apps and use voice commands to check their balances. (Forbes,2018) Moreover, according to the Finextra, AI is going to make a game-changing impact in the fintech industry. However, there are various opinions on the future of AI in Fintech. An article from the Forbes claims that people need to be aware of the risks of using AI technology in the financial sector. It says that increasing reliance on AI comes with a reduction in jobs.(Forbes,2017) On the other hand, the author of an article of finextra strongly argues that “With massive funding and intense competition, AI is the only way Fintech companies can stay ahead in the race in 2018”.

In order to understand more about who are interested in this field and how people respond to using AI in the financial sector, I analyzed #fintech on Twitter by using Netlytics and Gephi. As we can see in the hashtagify.me, Twitter and Instagram were the platforms that have a high popularity of #fintech (71.6 out of 100) and #AI(74 out of 100). Among those two platforms, Twitter was judged to be a more appropriate platform for analyzing because there were most active posts related to #fintech. I also gathered data from Instagram through Netlytics, but there were no meaningful topics that are closely related to AI and Fintech.

Text Analysis I did the text analysis of #fintech by using the tool called Netlytics. First of all, I looked up the words cloud.

Figure 1. Word cloud: The Top 100 most popular hashtags_netlytics

The word cloud shows that fintech is one of the popular topics on Twitter. #fintech was mentioned 996 times on tweets. The frequency of #ai is 307 and this indicates that fintech is a topic that is related enough to artificial intelligence, our main topic. #blockchain (516) #bitcoin (173) #crypto (208) and #cryptocurrency (152) were also a lot used in twitter. This represents that people or organizations who use twitter nowadays are interested in the field of cryptocurrencies such as bitcoin among many other sectors of fintech. Following graph presents the top 10 hashtags related to the #fintech.

Figure 2. Top 10 Hastags with high frequency by using Excel

I found it interesting to have a #healthtec in word cloud as a related hashtags. It has less frequency (40) compare to top 10 hashtags but still it was interesting because it seems to have no close relationship with #fintech at all. Therefore I looked up specific tweet related to #healthtech.

By closer looking at one specific tweet, I could find out that people often tweets related articles from medium with the keyword. The reason became clear why #healthtech, which seem to have no big relationship with #fintech, can be found in the word cloud.

Figure 3. Words over time

The thickest purple bar of #fintech in figure 3 shows that #fintech has been the most used word over time. Although #bitcoin has been also used a lot, overall, it is clear that fintech has been used constantly the most.

Figure 4. Top 10 Posters Mentioned in Messages (Netlytics)

According to the Netlytic Report, Top 10 Posters mentioned in messages are ipfcoline1 (#mentions: 70), andi_staub (#mentions:36), thomaspower (#mentions:27), syscoin (#mentions: 16), fintechna (#mentions: 16), financialbrand (#mentions: 13), motorcycletwitt (#mentions: 10), therudingroup (#mentions: 9), platinumcpromo (#mentions: 7), enricomolinari (#mentions: 6). Ipfcoline1, the user who was most mentioned in messages is an Info-Plus Formation Center, a computer consultant in France. Among 10 posters, 5 accounts are experts (andi_staub, thomaspower, motorcycletwitt, therudingroup, enricomolinari), 3 accounts are organizations (ipfcoline1, syscoin, platinumepromo) and 2 accounts are media. This represents that the experts and organizations that are related to the computer and financial fields are interested in #fintech.

Figure 5. Categories (Netlytics) This image represents that the most of the tweets that have #fintech belong to the category called ‘feelings (good)’. There are only ‘good’ feelings and words such as ‘great’(45), ‘good’(12), ‘proud’(12) are included. Network Analysis Netlytics

Figure 6. Who Mentions whom (Netlytics)

Figure 7.Who replies to whom (Netlytics) The networks image in Netlytics shows ‘Who mentions whom’ and ‘Who replies to whom’ in twitter. In the graph of ‘Who mentions whom’, there are a few big nodes in the center of the image which have connections with lots of other smaller nodes. Network graph in Netlytics shows that the “who mentions whom” graph is dense, while who replies to whom is not. In the network analysis in Gephi, it shows clearer structure of connections between accounts.

#hayoung #netlytics #fintech

Netlytic Analysis of #MachineLearning

For the following analysis, multiple tools were used to investigate the hashtag machinelearning. In this, the reader will get an insight of the term machine learning.

Figure 1: Word Cloud of #Machinelearning The following data collection was conducted on May 23, 2018. To conduct a textual analysis of the hashtag machinelearning on Twitter, the tool Netlytics was used. First, a word cloud was created which shows the top 50 words used concerning machine learning. The word cloud shows that machine learning is a quite popular topic on Twitter, as it’s mentioned 576 times, alongside with, #AI (514 times), #bigdata (213 times), and #iot (252 times).

Top 10 Most Frequent Used Words To get a clearer view of which words are most frequently used in combination with machine learning, the bar chart below shows the top 10 most frequently used words in combination with the hashtag.

Figure 2: Top 10 Most Frequent Used Words in relation to #Machinelearning Top 10 Posters The following pie chart will give an overview of the top 10 posters of the hashtag machinelearning. This pie chart shows that the number 1 user of the hashtag takes up most of the topic, with 31,5% just for the one account. This account is @machinelearn_d, and after follow up research, it makes sense as this is a twitter account dedicated to machine learning. The other posters in the 69,5% are almost evenly divided over the other 9 posters of the top 10.