Multi Class Review Rating Classification using Deep Recurrent Neural Networks
Review Rating Classification System Using Artificial Intelligence

Our research article titled “Multi Class Review Rating Classification Using Deep Recurrent Neural Networks” was published in an international journal “Neural Processing Letters” on 15 October 2019. In this tutorial, we briefly discuss the objectives, short summary, key contributions, and main findings presented in the article.

forward

Code Example To Generate Word Cloud Using R – Data Analysis

Word cloud help us to understand and visualize important keywords in given textual data sets. R is a powerful programming language used for exploration and visualization of data. Following code snippet can be used to generate word cloud using R programming language. [js] install.packages("tm") // package for text mining install.packages("wordcloud") // to generate word cloud install.packages("RColorBrewer") // to add colors in the word cloud library(tm)     // loading tm package library(RColorBrewer) // loading RColorBrewer package library(wordcloud) // loading wordcloud package text_data <- read_csv("data.csv") // reading data from csv file text <- text_data$col_name      // extracting data from column 'col_name'...
forward

Useful R Packages for Data Analysis

R is a powerful programming language used for exploring and analyzing data effectively. R provides many built in functions for data analysis. Furthermore there are many other R packages for data analysis which can extend the data analysis functionality. Following are some useful R packages which can be installed for specific tasks. Twitter Data Analysis: //rtweet.info install.packages(rtweet) Text Mining: install.packages("tm") // for text mining install.packages("SnowballC") // for text stemming install.packages("wordcloud")  // word-cloud generator install.packages("stopwords") // for multilingual stop words Colors: install.packages("RColorBrewer") // to add colors Visualization: install.packages("ggplot2") // for data visualization functions  ...
forward

Useful R Functions – Exploratory Data Analysis

R is a programming language used for statistical analysis and exploratory data analysis projects. According to the official website: R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. [source] Following are some useful R functions which can be used for data exploration and visualization. To read data from CSV file: data_obj <- read_csv("data.csv") In above line data_obj is the object name in which your data will be saved, data.csv is the...
forward

Limitations of Social Media Analysis for Participatory Urban Planning Process

In previous post we discussed how social media participatory process can help city designers, planners or administrators in decision making process. In this post we discuss the limitations / shortcomings of social media participatory process for urban planning. This post is a short summary of the paper titled as 'Missing intentionality: the limitations of social media analysis for participatory urban design' by Luca Simeone. Objective: The objective of this case study was to find limitations of social media analysis for participatory urban planning process. They analysed what city inhabitants are publishing on their social media profiles to perceive what they think...
forward

Social Media Participation in Urban Planning

Social media is generating a huge amount of data every second and this data can be used to make important decisions about any particular topic. This post is a short summary of a research paper titled as 'SOCIAL MEDIA PARTICIPATION IN URBAN PLANNING: A NEW WAY TO INTERACT AND TAKE DECISIONS' by E. López-Ornelas, R. Abascal-Mena, S. Zepeda-Hernández. Textual analysis is performed on social media data to know the sentiments (opinions) of the people about any topic. This type of analysis is very important for organizations and institutions to make important decisions. In this paper they have used the installation of a...
forward

5 Steps involved in Data Analysis Process

Data science deals with large amount of data and data scientist analyse that data to extract useful information from that data. This data analysis process involve 5 steps (1). In this post we will discuss those 5 steps involved in data analysis process and further we will explore some of the challenges we face during each step. In previous post we concluded that data science is the mixture of computing methods and statistical methods. In both data science and statistics, the core objective is to analyse the data. But in data science we automate some of the steps involved in...
forward

Introduction to Data Science (Notes)

Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. Data Science is a super-set of the fields of statistics and machine learning (1). DSI (Data Science Initiative, 2015) website,  gives us an idea about what Data Science is : "This coupling of scientific discovery and practice involves the collection, management, processing, analysis, visualization, and interpretation of vast amounts of heterogeneous data associated with a diverse array of scientific, translational, and interdisciplinary applications." Data Science vs Statistics: According to Data Science Association’s...
forward