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Big Pockets Real Estate has been the force behind making investors around the world, proud homeowners over the last 17 years and managed to win numerous hearts and awards along the way. We are the only performance-driven, international real estate agency. Focused on delivering the best of properties and dealing experiences across the world.we are currently the best international brokerage agency and have put in mountains of hard work to reach the top. Our motto is to convert properties into homes and clients into friends. Here is an overview of our growth trajectory for the last 17 years.
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We are a team of young and dynamic digital media experts, social media natives, event management gurus and veteran moguls of the real estate industry all under one roof. As a young and digital media savvy team, with entrepreneurial attitudes, we create a pool of marketing strategies that no other digital agency can boast of.
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Artificial Intelligence is a hot selling cake in Information Technology Sector these days. It is very powerful and can help B2B sector to get actionable insight from thousands of customers reviews. Whenever a new product is launched in the market it becomes quite tedious to get feedback and even if you become successful in gathering feedback through Survey responses still there is a long way to go.
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World Bank hosted its poverty prediction competition on the competition hosting website drivendata.org. The link to the competition is here. We decided to try out our Machine Learning skills on this dataset. Most regular work in ParallelDots is around three themes: Visual Analytics on images and videos, Healthcare AI and NLP, all three of which are solved using Deep Learning techniques.
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How NLP is Automating the complete Text Analysis Process for Enterprises?
In a world where we generate 2.5 quintillions (number of zeros = 18!) bytes of data every day, text analysis has become a key tool for structuring the data and getting the key insights. The organized and insightful data is worth millions of dollars in the present day scenario and it is no secret that Uber and Airbnb are so successful because of their massive data advantage. Harnessing data effectively enables companies not only to control costs and risks but also to compete more effectively and drive profitability by serving their end customers efficiently.
However, it’s easier said than done. Most of the organizations struggle to categorize the unorganized data and generate insights based on it. Not only the textual data but also the images, audios, and videos have become an integral part of information sharing in this digitally-driven world. Cleaning, tagging and converting this data into meaningful insights has added a level of sophistication in the way text analysis is being handled these days.
Earlier, it used to be nearly impossible for small companies to get hands on this kind of text analysis as either the tools available in the market were too overpriced or had to resort to low-end text mining giving them just a slice of the big pie. But the emerging technologies and the constant effort of the people to beat all odds has produced surprising results. The advent of NLP (Natural Language Processing) has armed each and every company with the means to analyze a plethora of data they have It empowered them to automate most of the processes involved in it thereby enabling them to directly fetch actionable information and thus saving both time and human cost.
Natural Language Processing (NLP) is the machine handling of written and spoken human communication. It consists of methods drawn on linguistics and statistics, coupled with machine learning, to model language in the service of automation. NLP employs a variety of methodologies to construe the ambiguities in human language, including the following: automatic summarization, part-of-speech tagging, sentiment analysis, feature extraction, relations extraction, as well as emotion detection. It takes into account all types of data gathered and fed, be it as simple as text or as abstruse as video files.
There are myriad applications of NLP when clubbed with text mining for businesses (or personal needs). Be it speech or text — with volume, velocity, or complexity sufficient to push you to seek an automated assistance — both can benefit from Natural Language Processing (NLP). Just imagine how dedicated algorithms can change the face of 80% unstructured business-relevant information around us. Moving forward I will try to illustrate the basic implementation and use case of various facets of NLP and how it can help us in text analysis.
Topics, Grammar, and Similarities
With the use of various statistical algorithms, various categories are determined which in more technical terms can be termed as “Classes of similarity”. Classification can be explained as the process by which various instances are clustered together into various classes (or groups) on the basis of various attributes. Generally, grouping can be of two types – one is conceptual classes, for instance, “smartphone companies” from Samsung, Nokia, Apple, Xiaomi, etc. Another class involves co-referencing – grouping similar instance in different categories of different classes. For example, “Lionel Messi is the captain of Barcelona FC. He was born in Argentina.” can refer to a different subset. It can be classified under Gender, Role, Nationality and millions of different classes. Even “He” word being used to refer Lionel Messi is also a information. One of the prominent methods of discerning relationships among entities is Syntactic Parsing.
Another lucky to have the feature of NLP in the world of text analysis is its spell and grammar check capabilities. Unlike Microsoft Word(or Google Docs) inbuilt spell checker, NLP based grammar and spell checkers are not limited to single error detection. For instance, normal spell check won’t identify two errors in “I went there at three o’clock.” Try using Stylus, on the other hand, one of the prominent interactive proofreading interfaces. A linguistic approach to grammar checking might involve resolving parts of speech. The process involves steps like sentence diagramming, part-of-speech tagging and study of syntactic relations.
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In a world where we generate 2.5 quintillions (number of zeros = 18!) bytes of data every day, text analysis has become a key tool for structuring the data and getting the key insights. The organized and insightful data is worth millions of dollars in the present day scenario and it is no secret that Uber and Airbnb are so successful because of their massive data advantage.
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Understand the social sentiment of your brand, product or service while monitoring online conversations. Sentiment Analysis is contextual mining of text which identifies and extracts subjective information in the source material.
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ParallelDots is one of the best applied AI research groups in the world. We work with enterprises globally to tackle challenging business problems and create the winners of tomorrow. We also provide AI consulting services to explore the "what, why, how and who" about deploying AI in businesses.
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SmartReader - An AI-powered feedback analysis platform
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The internet is filled with tutorials to get started with Deep Learning. You can choose to get started with the superb Stanford courses CS221 or CS224, Fast AI courses or Deep Learning AI courses if you are an absolute beginner. All except Deep Learning AI are free and accessible from the comfort of your home. All you need is a good computer (preferably with a Nvidia GPU) and you are good to take your first steps into Deep Learning.
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Machine learning has made it more accessible to create meaningful insights in a data-rich world. This includes data from customer surveys, qualitative primary research, and online verbatim comments. There is a wide range of input that arises in the lifetime of a business. This data needs to be mined for actionable insights, that can significantly impact the brand value of a business.
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In the past year, we launched multiple AI-based products. With a strong community of more than 25,000 registered users for our APIs and plugins, we are evolving every day. A persistent need that we uncovered from engaging with our community is – how can I make sense of the 1000s of customer feedback data quickly.
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Pulling out context from the text is one of the most remarkable procurements obtained using NLP. A few years back, context extraction was to detect the sentiment from the text and then the definition took a step forward towards emotion detection. These two are very different terms. The sentiment can be positive, negative, neutral while emotions are more refined categories among these three. A positive sentiment could be attributed to happy, excited and even a funny emotion. Similarly, anger, disgust, and sad emotions make the sentiment negative.
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AI in Marketing is playing a vital role in the complete automation of marketing.2016 was an important year for Artificial Intelligence. The wonderful capabilities of AI were foreseen by many people. But AI made a stellar announcement of its arrival when Google’s AlphaGo beat 18-time world champion Lee Sedol in a five-game Go match. AI is becoming one of the most invested areas of recent times.
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Machine Learning Practitioners have different personalities. While some of them are “I am an expert in X and X can train on any type of data”, where X = some algorithm, some others are “Right tool for the right job people”. A lot of them also subscribe to “Jack of all trades. Master of one” strategy, where they have one area of deep expertise and know slightly about different fields of Machine Learning. That said, no one can deny the fact that as practicing Data Scientists, we will have to know basics of some common machine learning algorithms, which would help us engage with a new-domain problem we come across.
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In one of our last blog post, we discussed how to identify ‘Fake Accounts’ or ‘Potential Spammers’ on Twitter. It is important to filter out such information to get most reliable and accurate insights. A lot of firms and individuals have taken the game forward and used Twitterbots to automate and fasten the content delivery. A study estimated that the number of active bots on twitter can be as high as 15% of the total users.
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The first step towards checking the pulse of your business is asking existing customers the right questions.Most enterprises get this right.It is what they have done with all this data that has made all the difference.Qualitative answers are as rich in insight as they hard to mine manually. Fortunately, the latest advances in artificial intelligence have made it possible for almost anyone to quickly analyze large and open-ended responses. One such tool is the Google Sheets add-on by ParallelDots. It lets you perform survey analysis in your own spreadsheet and is very straightforward to apply.
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