Data Analytics

Analyzing the impact of user-generated content on B2B Firms' stock performance: Big data analysis with machine learning methods: sciencedirect.com/science/ar…

Are brand-targeting social #bots distorting information? A #bigdata analytics of #brand social bots on Twitter: researchgate.net/publication…

This JBR article uses big data analytics to derive quantifiable luxury insights (doi.org/10.1016/j.jbusres.20…). The top 15 luxury brands with the highest number of Twitter followers, identified from PMX Agency's (2017) list of luxury brands per Twitter follower counts, were included.

Chinese power in artificial intelligence: inc.com/magazine/201809/amy-…

Artificial Intelligence: history and current surge in popularity: sas.com/en_us/insights/analy….

Big data, #ArtificialIntelligence and machine learning are used extensively in auto driving @Tesla: forbes.com/sites/bernardmarr…

Linear algebra for machine learning 4: singular value decomposition (#SVD) for Term-document matrix. Let r be the rank of the M× N matrix C. Then: mathworld.wolfram.com/Singul…

Linear algebra for machine learning 3: for a symmetric matrix, eigenvectors corresponding to different eigenvalues are orthogonal. In addition, if all its entries are real, then its eigenvalues are real.

Linear algebra for machine learning 2: Let A be M*M matrix: Ax = λx. N-vector x is called right eigen vector. x corresponding to eigenvalue of the largest absolute value is principal eigenvector.

Linear algebra for machine learning 1: Let A be a M*N matrix, the rank of A is the number of linearly independent columns or rows: rank(A)<=min(M,N).

To get actionable insights from big data from #IoT and argument reality, data reduction with much loss of information is critical. Hence, matrix decomposition methods, such as #SVD and #PCA, are useful. I'll post a series of shorts notes on must-have linear algebra operations.

Use the sci-kit in #Python with good machine learning and #NLP models.

The graphical network model will come in handy when analyzing the networked societal effect of modern #IoT and social networks.

#Bigdata of brands, products and services from social media and #IoT requires new methods in data collection, analysis and visualization

Artificial intelligence can inadvertently magnify the herding impact of human tendency to follow the influential and powerful.

We can also use correspondence analysis to visualize contingency table with aggregated categorical social big data of user generated content

Image courtesy at FreeDigitalPhotos.net

Some believe market structure analysis is a critical task for marketers.

Social #bigdata of user generated content can give actionable brand insights when combined with business context and machine learning models