Before diving into VADER, let us first understand what ‘Sentiment Analysis’ is? Sentiment Analysis also known as** ‘Opinion Mining’.**Sentiment Analysis is the analysis of a text (word or sentence or a document) as a ‘positive’ , ‘negative’ or ‘neutral’. Few analyzers might not take the label ‘neutral’ into consideration as it merely depends on the business requirements. Sentiment analysis is used in analyzing the sentiment score of the movie reviews, food reviews, speech reviews, etc.
(Note: In few cases, movie reviews, food reviews, etc, might not have direct positive and negative ratings. It might have ratings like 1, 2, 3, 4, 5…, that defines the level of positivity or negativity of a text review. Based on the business requirements and easy analysis, the analyzer might set the range of ratings to label as positive and negative. For example: if we have ratings 1, 2, 3, 4, 5, and analyzer might set the review as positive if it has ratings 1, 2, or 3, and negative otherwise.)
VADER stands for ‘Valence Aware Dictionary and sEntiment Reasoner’.(Note: in the spelling ‘sEntiment’, first letter ‘s’ is a small letter and second letter ‘E’ is capital and it is correct). VADER is a lexicon and rule-based sentiment analysis tool. It is used to analyze the sentiment of a text. Lexicon is a list of lexical features (words) that are labeled with positive or negative based on the semantic meaning. Even an unlabelled text data can be labeled with VADER sentiment analyzer.
Open cmd prompt or anaconda prompt and type in ‘pip install vaderSentiment’
Installation of vaderSentiment
Or in the local jupyter notebook as shown in the below image or in any other notebook you are using, just type in **‘!pip install vaderSentiment’**and run the line. (Note: As I have already installed it, it is giving ‘Requirement already satisfied….’)
Installation of vaderSentiment
Example — 1
We first need to import nltk (Natural Language Toolkit) and then download ‘vader_lexicon’. Refer to the below image for the code.
Now we need to import the VADER Sentiment Analysis from nltk. Then create an instance for the imported library.
#audio #signal-processing #speech-recognition #conversational-ui #naturallanguageprocessing
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.
Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.
Now, addressing the main topic of interest – how are data analysis and data science different from each other.
As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –
#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.
#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis
EDA is a way to understand what the data is all about. It is very important as it helps us to understand the outliers, relationship of features within the data with the help of graphs and plots.
EDA is a time taking process as we need to make visualizations between different features using libraries like Matplot, seaborn, etc.
There is a way to automate this process by a single line of code using the library Pandas Visual Analysis.
Let’s understand the different sections in the user interface :
#data-analysis #machine-learning #data-visualization #data-science #data analysis #exploratory data analysis