Automation of Sentiment Analysis & Topic Modeling on Py-Spark & SparkNLP

“Elections nowadays aren’t the same, social-media have changed them a lot!” — Oh yes, me.

Over the past decade, usage of political social-media (mainly Twitter) accounts has skyrocketed. Many political leaders (& sometimes their families) are using Twitter as a preeminent mode of communication with their citizenry. However, this has led to some interesting problems. Not only American elections but also the recent elections of the world’s largest democracy, India, was also accused to be **‘Biased’ **due to social media influence (check out this article by ‘The Washington Post’ to get what I mean here). The bias, mainly in the form of polarized ‘public sentiments’, was injected by distorting the fragile fabric of social-media.

Thinking of American politics & Twitter, chances are President Donald J. Trump comes to your mind. Ever since the year 2015, when Trump launched his political campaign, he became infamous for his so-called negative, derogatory & somewhat provoking tweets. Give him, 280 character limit, he’ll translate it to a package consisting of the whole spectrum of emotions, sentiments, facts, and opinions (check out this article by ‘The New York Times’ to know the bulk of tweets which he plays with). Even _Vox _(famous American news and opinion website), in one of its articles, confirms that Trump tweets a lot, & the quantum is really out there.

All of the above facts, combined with my advanced-analytics knowledge made me think — can I develop a Live App that could keep a track on the social-media behavior of candidates fighting for the United States Presidential election, 2020?

#automation #topic-modeling #machine-learning #pyspark #nlp

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Automation of Sentiment Analysis & Topic Modeling on Py-Spark & SparkNLP

“Elections nowadays aren’t the same, social-media have changed them a lot!” — Oh yes, me.

Over the past decade, usage of political social-media (mainly Twitter) accounts has skyrocketed. Many political leaders (& sometimes their families) are using Twitter as a preeminent mode of communication with their citizenry. However, this has led to some interesting problems. Not only American elections but also the recent elections of the world’s largest democracy, India, was also accused to be **‘Biased’ **due to social media influence (check out this article by ‘The Washington Post’ to get what I mean here). The bias, mainly in the form of polarized ‘public sentiments’, was injected by distorting the fragile fabric of social-media.

Thinking of American politics & Twitter, chances are President Donald J. Trump comes to your mind. Ever since the year 2015, when Trump launched his political campaign, he became infamous for his so-called negative, derogatory & somewhat provoking tweets. Give him, 280 character limit, he’ll translate it to a package consisting of the whole spectrum of emotions, sentiments, facts, and opinions (check out this article by ‘The New York Times’ to know the bulk of tweets which he plays with). Even _Vox _(famous American news and opinion website), in one of its articles, confirms that Trump tweets a lot, & the quantum is really out there.

All of the above facts, combined with my advanced-analytics knowledge made me think — can I develop a Live App that could keep a track on the social-media behavior of candidates fighting for the United States Presidential election, 2020?

#automation #topic-modeling #machine-learning #pyspark #nlp

Streamline Your Data Analysis With Automated Business Analysis

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.

Workflow integration and AI capability

Pinpoint unexpected data changes

Understand customer behavior

Enhance marketing and ROI

…

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Sentiment Analysis in Python using Machine Learning

Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?

We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.

But why do we need sentiment analysis?

Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.

It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.

Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.

Lastly, they are also used for spam filtering and removing unwanted content.

How does sentiment analysis work?

NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.

NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.

Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.

Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are

• Simple classification of text into positive, negative or neutral. It may also advance into fine grained answers like very positive or moderately positive.
• Aspect-based sentiment analysis- where we figure out the sentiment along with a specific aspect it is related to. Like identifying sentiments regarding various aspects or parts of a car in user reviews, identifying what feature or aspect was appreciated or disliked.
• The sentiment along with an action associated with it. Like mails written to customer support. Understanding if it is a query or complaint or suggestion etc

Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.

• Matching rules based sentiment analysis: There is a predefined list of words for each type of sentiment needed and then the text or document is matched with the lists. The algorithm then determines which type of words or which sentiment is more prevalent in it.
• This type of rule based sentiment analysis is easy to implement, but lacks flexibility and does not account for context.
• Automatic sentiment analysis: They are mostly based on supervised machine learning algorithms and are actually very useful in understanding complicated texts. Algorithms in this category include support vector machine, linear regression, rnn, and its types. This is what we are gonna explore and learn more about.

In this machine learning project, we will use recurrent neural network for sentiment analysis in python.

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Top Spark Development Companies | Best Spark Developers - TopDevelopers.co

An extensively researched list of top Apache spark developers with ratings & reviews to help find the best spark development Companies around the world.

Our thorough research on the ace qualities of the best Big Data Spark consulting and development service providers bring this list of companies. To predict and analyze businesses and in the scenarios where prompt and fast data processing is required, Spark application will greatly be effective for various industry-specific management needs. The companies listed here have been skillfully boosting businesses through effective Spark consulting and customized Big Data solutions.

Check out this list of Best Spark Development Companies with Best Spark Developers.

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