Week 4 Response to Discussion 1 & 2 BUS 624/625
kingstonman41
BUS625Week4ResponsetoDiscussion2.docxBUS 625 Week 4 Response to Discussion 2
Guided Response: Your initial response should be a minimum of 300 words in length. Respond to at least two of your classmates by commenting on their posts. Though two replies are the basic expectation for class discussions, for deeper engagement and learning, you are encouraged to provide responses to any comments or questions others have given to you.
Below there are two of my classmate’s discussion that needs I need to response to their names are Umadevi Sayana
and Britney Graves
TuesdayMar 17 at 7:50am
Twitter mining analyzed the Twitter message in predicting, discovering, or investigating the causation. Twitter mining included text mining that designed specifically to leverage Twitter content and context tweets. With the use of text mining, twitter was able to include analysis of additional information that associates to tweets, which include hashtags, names, and other related characteristics. The mining also employs much information as several tweets, likes, retweets, and favorites trying to understand the considerations better. Twitter using text mining was successful in capturing and reflecting different events that relate to other conventional and social media. In 2013, there were over 500 million messages per day for twitter and became impossible for any human to analyze. It became important than to develop computer-based algorithms, including data mining. Twitter implements text mining in analyzing the sentiment that associates with twitter messages. It based on the analysis of the keyword that words are having a negative, positive, or neutral sentiment (Sunmoo, Noémie& Suzanne, (Links to an external site.)n.d). Positive words, for example like great, beautiful, love, and negative words of stupid, evil, and waste, do regularly have lexicons. Using text mining, Twitter was able to capture sentiments by capturing many dictionary symbols. Moreover, the sentiment applied to abbreviations, emoticons, and repeated characters, symbols, and abbreviations.
The sentiments on topics of economics, politics, and security are usually negative, and sentiments related to sports are harmful. Twitter also used text mining to collect and analyze for topic modeling techniques over time. To pull out the data from Twitter, TwitterR used. “Someone well versed in database architecture and data storage is needed to extract the relevant information in different databases and to merge them into a form that is useful for analysis” ( Sharpe, De Veaux & Velleman, 2019, p.753). It provides the interface that connects to Twitter web API; retweetedby/ids also used combined with RCurl package in finding out several tweets that retweeted. Text mining is also used in Twitter to clean the text by taking out hyperlinks, numbers, stop words, punctuations, followed by stem completion. Text mining also implemented for social network analysis.
Web mining focus on data knowledge discovery of data from blogs, online mailing lists, social media, including the structure analysis, content, and usage. Web mining aim in extracting and analyzing the information that is useful from the content of the web through several techniques from data mining, natural language processing, machine learning. In Twitter, web mining is used in selecting keywords, importing the data, preparing, analyzing, and interpreting the data. For example, a web content mapping for physical activity includes searching for keywords like body fat, body mass index, appetite, obesity, overweight, and importing data in searching the Twitter database specifying a period. The preparation of data includes cleaning the extraneous words and analyze the data by calculating frequency vectors that result in terms like circumference, supplements, calculator, and height. The web mining method on Twitter is also used in social media to study health behaviors. It is essential to understand the behaviors that are difficult due to the complexity. The web mining in twitter help to reveal the situational context of before and after the physical activity (Sunmoo, Noémie& Suzanne , (Links to an external site.)n.d). The analysis provides the situational context purposes like build muscle, time with words like now, today, social context, words like gym, environmental with water trial. Tweets capture the detailed fair information measurement as several calories burned. Web mining content also used to track the mobility changes of the microblogging context. It relates to the fact in which the user is no longer bound to the computer while generating microblogging content (Mathieu & Derek, n.d). Web mining helps to find the effect of mobility level on features of the user in the dataset of their followers, followees, and recent 100 recent posted tweets using Twitter User API. It allows us to identify a total number of followees and followers in the Twitter applications and web pages.
Twitter got benefitted with the text and web mining that help to achieve a large number of customers that ta re satisfied and increase customer loyalty. Mining help in overcoming risk factors and display hidden profitability (Maningo, 2020). Mining helps the reduction of client’s involvement with proper extraction and analysis of client data. It helps Twitter to identify customer groups to market the different products according to the niche.
References:
Mathieu, P. & Derek, R. (n.d). The effect of Mobile platforms on Twitter content generation. file:///C:/Users/TZ97TH/Downloads/2798-14225-1-PB.pdf
Maningo, J. (2020, February 6). How to Use Twitter for Data Mining. https://www.quickstart.com/blog/how-to-use-twitter-for-data-mining/ (Links to an external site.)
Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019). Business statistics (4th ed.) . https://www.redshelf.com
Sunmoo, Y., Noémie, E., & Suzanne, B. (Links to an external site.) (n.d). A Practical Approach for Content Mining of Tweets. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694275/
WednesdayMar 18 at 4:48pm
Before we can understand how a company benefits from text mining, it’s essential to know what that is and how it can be used. Text mining is using text to obtain quality information instead of relying on trends and relationships that are solely from numbers. This type of data can provide a diverse set of data because every person has different experiences and insight that they can bring to a company’s attention. Chase Bank uses text data by analyzing call center transcripts and tracking and responding to online reviews and being active on Twitter and other social media. Chase Bank’s twitter account is continuously asking for feedback, and responding to tweets left my customers regardless of if customers are satisfied or not. Now, Chase can gather this information, turn it into numbers, and can see a visual representation of text. (Bennett, 2017)Explains that “The process by which text mining solves the problems of structure and scale is where data science comes in. The basic approach is to turn text into numbers so that we can use machines to analyses the large volumes of documents and discover insights through mathematical algorithms” (p. 3). Text mining can provide equally important information by being equated into numbers. Web mining has a lot of benefits as well because it allows companies to find and use information from the web to predict behavior and improve customer relations. For example, how often customers are clicking hyperlinks, how often they visit websites, and the content of web pages. Chase Bank uses this information every time they send out an email with a promotion or a new product and attach a hyperlink. How many customers are click this link? Are more people clicking the email link or the texted link? While this doesn’t seem like important information, knowing this data can help Chase Bank recreate its business plan to make a shift from an email hyperlink to a text message. Through web mining, Chase Bank may discover that people are near a specific area code are searching for the nearest location; therefore, they can see there is a demand for their services.
Reference
Bennett, F. (2018, July 15). What is text mining and how can it be used to create value for business? Retrieved March 17, 2020, from https://www.mastodonc.com/2017/04/12/what-is-text-mining-and-how-can-it-be-used-to-create-value-for-business/
