I recently worked on a text classification project which used Tweets that included sentiment labels. I was curious how these human-provided labels would differ from popular sentiment detection tools. I chose two tools, VADER and Text Blob, and ran a little experiment. You can find code for the complete experiment here, but I’ll include a few code snippets as I go along.
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Customer feedback is great. But have you been able to turn that feedback into meaningful customer insights? A few years back, brands depended on surveys to gauge customers’ feelings about how their products were performing. From the product reviews, they were able to somehow get a grip on the general feeling of good, bad, or neutral response to their marketing campaign or product. There is, however, so much more information in the form of unstructured data that brands need to lay their hands on to better analyze the sentiments of their customers.
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Researchers in China say they’ve created sarcasm detection AI that achieved state-of-the-art performance on a dataset drawn from Twitter. The AI uses multimodal learning that combines text and imagery since both are often needed to understand whether a person is being sarcastic. The researchers argue that sarcasm detection can…
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Boston-based call analytics startup Cogito today closed a $25 million convertible debt round led by Goldman Sachs Growth Equity, Salesforce Ventures, and other new and existing investors. The company says this latest capital will be used to expand its go-to-market efforts and accelerate the development of new products.
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It is estimated that 80% of the world’s data is unstructured. Thus deriving information from unstructured data is an essential part of data analysis. Text mining is the process of deriving valuable insights from unstructured text data, and sentiment analysis is one applicant of text mining.
Read MorePlato’s Republic, which introduces questions that dominate western political philosophy even nowadays, is fundamentally a dialogue. Plato endeavours to conceptualize the ideal society through philosophical discussions and these tendencies for spirited debates are quite explicit in books I~II.
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A simple tutorial to analyse the sentiment of a book in Python and scikit-learn. In this tutorial, I will explain how to calculate the sentiment of a book through a Supervised Learning technique, based on Support Vector Machines (SVM).
Read MoreFeedback analysis with Lightgbm classifier. Objective: to analyze travelers ‘feelings’ on Twitter posts. It would be fascinating for airlines to use this free data to provide better service to their customers.
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The faster development of social networks is causing explosive growth of digital content. It has turned online opinions, blogs, tweets, and posts into a very valuable asset for the corporates to get insights from the data and plan their strategy.
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In this article, I would like to take our dataset a step further to solve a sentiment classification problem. Specifically, we will be assigning sentiment targets to each review and then using a binary classification algorithm to predict those targets.
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