Analyzing the chaotic Presidential Debate 2020 with text mining techniques

Thanks to the internet, now the world knew about the Presidential Debate 2020 that went out of control. All of the major news stations were reporting about how the participants were interrupting and sniping at one another.

I decided to put together an article that focuses on analyzing the words used in the event and see if there are any hidden insights.

This article focuses on finding out the most used words, categorized by each spokesperson, and sentiment analysis of the speeches.

The first 2020 Presidential Debate overview

Between:

• Incumbent President Donald Trump

• Former Vice President Joe Biden (Democratic nominee)

Moderator:

• Chris Wallace

Topics covered:

1. The candidates’ political records
2. The Supreme Court
3. The coronavirus
4. The economy
5. Race and violence in cities
6. The integrity of the election

Cleaning the dataset

In total, close to 20,000 words were used in the event. After removing names and common stop words, around 6000 words were left for analysis.

#tokenize
text_df <-  text %>%
  unnest_tokens(word, Text)

#Remove stop words
my_stop_words <- tibble(
  word = c("chris","wallace","trump","donald","joe","biden","vice","president"))
#Prepare stop words tibble
all_stop_words <- stop_words %>%
  bind_rows(my_stop_words)
textClean_df <- text_df %>%
  anti_join(all_stop_words, by = "word")

#donald-trump #data-analysis #politics #joe-biden #data-science

What is GEEK

Buddha Community

Citrix Bugs Allow Unauthenticated Code Injection, Data Theft

Multiple vulnerabilities in the Citrix Application Delivery Controller (ADC) and Gateway would allow code injection, information disclosure and denial of service, the networking vendor announced Tuesday. Four of the bugs are exploitable by an unauthenticated, remote attacker.

The Citrix products (formerly known as NetScaler ADC and Gateway) are used for application-aware traffic management and secure remote access, respectively, and are installed in at least 80,000 companies in 158 countries, according to a December assessment from Positive Technologies.

Other flaws announced Tuesday also affect Citrix SD-WAN WANOP appliances, models 4000-WO, 4100-WO, 5000-WO and 5100-WO.

Attacks on the management interface of the products could result in system compromise by an unauthenticated user on the management network; or system compromise through cross-site scripting (XSS). Attackers could also create a download link for the device which, if downloaded and then executed by an unauthenticated user on the management network, could result in the compromise of a local computer.

“Customers who have configured their systems in accordance with Citrix recommendations [i.e., to have this interface separated from the network and protected by a firewall] have significantly reduced their risk from attacks to the management interface,” according to the vendor.

Threat actors could also mount attacks on Virtual IPs (VIPs). VIPs, among other things, are used to provide users with a unique IP address for communicating with network resources for applications that do not allow multiple connections or users from the same IP address.

The VIP attacks include denial of service against either the Gateway or Authentication virtual servers by an unauthenticated user; or remote port scanning of the internal network by an authenticated Citrix Gateway user.

“Attackers can only discern whether a TLS connection is possible with the port and cannot communicate further with the end devices,” according to the critical Citrix advisory. “Customers who have not enabled either the Gateway or Authentication virtual servers are not at risk from attacks that are applicable to those servers. Other virtual servers e.g. load balancing and content switching virtual servers are not affected by these issues.”

A final vulnerability has been found in Citrix Gateway Plug-in for Linux that would allow a local logged-on user of a Linux system with that plug-in installed to elevate their privileges to an administrator account on that computer, the company said.

#vulnerabilities #adc #citrix #code injection #critical advisory #cve-2020-8187 #cve-2020-8190 #cve-2020-8191 #cve-2020-8193 #cve-2020-8194 #cve-2020-8195 #cve-2020-8196 #cve-2020-8197 #cve-2020-8198 #cve-2020-8199 #denial of service #gateway #information disclosure #patches #security advisory #security bugs

Hands-on Guide to Pattern - A Python Tool for Effective Text Processing and Data Mining

Text Processing mainly requires Natural Language Processing( NLP), which is processing the data in a useful way so that the machine can understand the Human Language with the help of an application or product. Using NLP we can derive some information from the textual data such as sentiment, polarity, etc. which are useful in creating text processing based applications.

Python provides different open-source libraries or modules which are built on top of NLTK and helps in text processing using NLP functions. Different libraries have different functionalities that are used on data to gain meaningful results. One such Library is Pattern.

Pattern is an open-source python library and performs different NLP tasks. It is mostly used for text processing due to various functionalities it provides. Other than text processing Pattern is used for Data Mining i.e we can extract data from various sources such as Twitter, Google, etc. using the data mining functions provided by Pattern.

In this article, we will try and cover the following points:

• NLP Functionalities of Pattern
• Data Mining Using Pattern

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#developers corner #data mining #text analysis #text analytics #text classification #text dataset #text-based algorithm

Analyzing the chaotic Presidential Debate 2020 with text mining techniques

Thanks to the internet, now the world knew about the Presidential Debate 2020 that went out of control. All of the major news stations were reporting about how the participants were interrupting and sniping at one another.

I decided to put together an article that focuses on analyzing the words used in the event and see if there are any hidden insights.

This article focuses on finding out the most used words, categorized by each spokesperson, and sentiment analysis of the speeches.

The first 2020 Presidential Debate overview

Between:

• Incumbent President Donald Trump

• Former Vice President Joe Biden (Democratic nominee)

Moderator:

• Chris Wallace

Topics covered:

1. The candidates’ political records
2. The Supreme Court
3. The coronavirus
4. The economy
5. Race and violence in cities
6. The integrity of the election

Cleaning the dataset

In total, close to 20,000 words were used in the event. After removing names and common stop words, around 6000 words were left for analysis.

#tokenize
text_df <-  text %>%
  unnest_tokens(word, Text)

#Remove stop words
my_stop_words <- tibble(
  word = c("chris","wallace","trump","donald","joe","biden","vice","president"))
#Prepare stop words tibble
all_stop_words <- stop_words %>%
  bind_rows(my_stop_words)
textClean_df <- text_df %>%
  anti_join(all_stop_words, by = "word")

#donald-trump #data-analysis #politics #joe-biden #data-science

The Debate Drinking Game, with data science

COVID-19 may have taken away our in-person debate watch parties, but it’s not stopping us from making a drinking game out of it! In my latest Youtube video, I used text mining techniques to develop the _ultimate _data-driven drinking game rules for the upcoming Presidential debates. This post will walk you through exactly how I did that.

To start, I scraped the transcripts from campaign rallies, speeches, and any other events that have taken place in the last few weeks during which Biden or Trump (or both!) spoke. The full list of events I scraped can be seen in the Github repo for this project (see the “debates.csv” file).

I scraped the transcripts from rev.com (with their permission!) because it seemed to have the most exhaustive list of election 2020 events, and because the transcripts followed a standardized format, which made the scraping process easier. Here’s the function I used to scrape the transcripts:

def scrapeTranscriptFormat1(url, sep):
    html = requests.get(url)
    html = html.text
    bs = BeautifulSoup(html, "lxml")
    paragraphs = bs.findAll("p")
    for paragraph in paragraphs:
        try:
            paragraph.find('u').decompose()
        except:
            continue
    speaker = []
    speech = []
    pattern = r'\[.*?\]'
    for paragraph in paragraphs:
        try:
            speechText = paragraph.text.replace(u'\xa0', u'') 
            speechText = re.sub(pattern, '', speechText) 
            if sep == "parenthesis":
                speech.append(re.search("[0-9]{2}\)[\r\n]+(.*)", speechText).group(1).strip(" ")) 
            else:
                speech.append(re.search(":[\r\n]+(.*)", speechText).group(1).strip(" ")) ## search for speaker's speech, append to list
            speaker.append(re.search("^(.*?):", speechText).group(1)) ## search for speaker name, append to list            
        except:
            continue
    return pd.DataFrame({'name': speaker, 'speech': speech})

#text-mining #text-analysis #data-science #presidential-debates #data-analysis

Laravel 7 Full Text Search MySQL

Here, I will show you how to create full text search in laravel app. You just follow the below easy steps and create full text search with mysql db in laravel.

Laravel 7 Full Text Search Mysql

Let’s start laravel full-text search implementation in laravel 7, 6 versions:

1. Step 1: Install Laravel New App
2. Step 2: Configuration DB .evn file
3. Step 3: Run Migration
4. Step 4: Install Full Text Search Package
5. Step 5: Add Fake Records in DB
6. Step 6: Add Routes,
7. Step 7: Create Controller
8. Step 8: Create Blade View
9. Step 9: Start Development Server

https://www.tutsmake.com/laravel-full-text-search-tutorial/

#laravel full text search mysql #laravel full text search query #mysql full text search in laravel #full text search in laravel 6 #full text search in laravel 7 #using full text search in laravel