The word soft may evoke positive connotations of warmth and cuddliness in many contexts, but calling a hockey player soft would be an insult. If you were to say something was terrific in the 1800s, this would probably imply that it was terrifying and awe-inspiring; today, terrific basically just implies that something is (pretty) good. A word's sentiment or connotation depends on the domain or context in which it is used. However, previous computational work in natural language processing largely ignores this issue, and focuses and building and deploying generic domain-general sentiment lexicons. SocialSent is a collection of code and datasets for performing domain-specific sentiment analysis.

If you are recruiting software engineers in the US and feel like you are in a crazy competitive field, well, you are right. Looking at LinkedIn data, software engineers are one of the most sought-after talent pools, with the average engineer receiving 3X as much recruiter interest on LinkedIn as the average member. They're also 13% less likely to apply for a job compared to everyone else. However, the good news is that software engineers are quite open to new opportunities and they are willing to hear you out. There are millions of them on LinkedIn in the US and they're 12% more likely to respond to a recruiter about a new job opportunity, compared to the average professional. In fact, about ¼ of these engineers changed jobs in the past two years, often taking considerable pay bumps when they moved to a new organization.

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Already, as of 2010, a quarter of Americans (24 percent) had posted product reviews or comments online, and 78 percent of internet users had gone online for product research. But those are ancient stats. More recently, BrightLocal found in 2016 that 91 percent of consumers regularly or occasionally read online reviews, with 47 percent taking sentiment of local-business reviews -- the tonality of a review's text -- into account in purchasing decisions. Breaking out the figures, 74 percent of consumers say that positive reviews make them trust a local business more, and 60 percent say that negative reviews make them not want to use a business, according to BrightLocal. So reviews are important, and the feelings expressed are key.

Machine learning and artificial intelligence are so difficult to understand, only a few very smart computer scientists know how to build them. But the designers of a new tool have a big ambition: to create the Javascript for AI. The tool, called Cortex, uses a graphical user interface to make it so that building an AI model doesn't require a PhD. The honeycomb-like interface, designed by Mark Rolston of Argodesign, enables developers–and even designers–to use premade AI "skills," as Rolston describes them, that can do things like sentiment analysis or natural language processing. They can then drag and drop these skills into an interface that shows the progression of the model.

As part of my Technology and Innovation MBA program at Ted Rogers School of Management, I took a data and knowledge management course which teaches students the principles and practices of knowledge management. The second part of the course delves on tools used in data management and analytics. Although the theoretical part of the course was a bit dry, the hands-on portion was very interesting and exposed students to several different tools to capture, clean and analyze data. One of the tasks given to students was to capture and analyze twitter data. Although students had access to Netlytics, which is a neat cloud-based text and social network analysis tool that also collects Twitter data, students were encouraged to find other ways to collect Twitter data.

Earlier we could match and extract the required information from the given text data using Ctrl F, Ctrl C, and Ctrl V. Isn't it? Probably, some of us still do it when the data is small. But this approach is slow and prone to lots of mistakes. In text analytics, the abundance of data makes such keyboard shortcut hacks obsolete. Because of the data volume and its complicated (unstructured) nature, we require much faster, convenient, and robust ways of information extraction from text data.

So, Naive Bayes gives very bad result. It can just predict 11% of bad comments. SGDClassifier predicted 47% of bad comments correctly which is a considerable improvement over the Naive Bayes. Logistic Regression though has regression in its surname but its a classifier and it shows good improvement over SGDClassifier. SVC comes out as winner with 66 % correct prediction for sentiment analysis.

This is the 6th part of my ongoing Twitter sentiment analysis project. You can find the previous posts from the below links. Before we jump into doc2vec, it will be better to mention word2vec first. "Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words."