In this project, I will demonstate how to perform sentiment analysis on tweets using various C# libraries. All of the code below will be placed in the Program class. Thanks to the Tweetinvi library, the authentication with the Twitter API is a breeze. Assuming that an application has been registered at http://apps.twitter.com, This type of global authentication makes it easy to perform authenticated calls throughout the entire application.

Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of the brand, product or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count based metrics. This is akin to just scratching the surface and missing out on those high value insights that are waiting to be discovered. So what should a brand do to capture that low hanging fruit? With the recent advances in deep learning, the ability of algorithms to analyse text has improved considerably.

Somewhere, someone is tweeting "[This airline] sucks the big one!" In the past, they would have been ignored. These days many airlines respond with sympathy ("We're so sorry you're having a rough trip -- please DM us, so we can resolve it") or send an invitation to call an 800-number (where you can wait on hold forever). A tool called sentiment analysis, or the mathematical categorization of statements' negative or positive connotations, gives companies powerful ways to analyze aggregate language data across all sorts of communications, not only tweets. Here are five of the most valuable sentiment sources to tap.

This video course will demonstrate the steps for analyzing unstructured data with the R/R Studio software. The approaches will be illustrated using practical applications for business, healthcare, and retail data, among others. At the end the video course you will have mastered obtaining and visualizing data with R. You will also be confident with data cleaning, preparation, and sentiment analysis with R. Dr. Bharatendra Rai is a professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne State University, Detroit.

The exponential growth of the web generates an exceptional quantity of data for which automatic knowledge capture is essential. This work describes the methodology for web-scale information extraction in the linked open data information-extraction (LODIE) project and highlights results from the early experiments carried out in the initial phase of the project. LODIE aims to develop informationextraction techniques able to scale at web level and adapt to user information needs. The core idea behind LODIE is the usage of linked open data, a very large-scale information resource, as a groundbreaking solution for IE, which provides invaluable annotated data on a growing number of domains. This article has two objectives, first, describing the LODIE project as a whole and depicting its general challenges and directions; and second, describing some initial steps taken toward the general solution, focusing on a specific IE subtask, wrapper induction. Nevertheless, the current state of the art has mainly addressed tasks for which resources for training are available (for example, the TAP ontology in the paper by Etzioni and colleagues [2004]) or use generic patterns to extract generic facts (for example, Banko et al. [2007]; OpenCalais.com). The limited availability of resources for training has so far prevented the study of the generalized use of large-scale resources to port to specific user information needs. The linked open data information-extraction (LODIE) project focuses on the study of IE models and algorithms able to perform efficient user-centered web-scale learning by exploiting linked open data (LOD). In this article we will highlight the initial steps of the LODIE project, focusing on a specific IE task, wrapper induction (WI), which consists of automatically learning wrappers for uniform web pages, that is, pages from one website, usually generated with the same script and all describing the same type of entity. We show results on the WI task, exploiting linked data obtained from DBpedia as learning material.

Many common mistakes can be avoided when testing sentiment data for predictive properties. The term "prediction" is not a legal definition. In assessing the predictive qualities of sentiment data there are no rules for what counts as a signal to be tested for predictive properties with regard to financial assets. However, the method you chose ultimately defines what you mean with the term "prediction". To illustrate the point: Using a more prudent definition of the term, the accuracy in the world's most famous prediction study could have been as low as 47% (7 out of 15) instead of 87% (13 out of 15%).

In essence, sentiment analysis is the process of gauging the emotional tone behind a series of words, used to gain an understanding of the emotions, attitudes and opinions expressed within a customer's online mentions. Real-world examples include the Obama administration using SA to measure public responses to campaign messages ahead of 2012 presidential election, and Expedia Canada taking advantage of SA to quickly understand negative consumer attitudes to the music used in one of their adverts.

Not many would have predicted the hype around these technologies in the last few years. But, given the time of year, I'm going to try and make some predictions for the 2018. My first prediction is that large companies will push for automated models to drive their critical business processes just like is currently being done in credit scoring and in direct marketing. This leads me to my second prediction. To enable this drive to automate, companies will need to scale their Data Science and Machine Learning efforts by developing specialized roles for data engineering, data science and model deployment.

Check out "Text Mining with R: A tidy approach" to learn about how tidy data principles and the tidytext package can help you perform text mining in R. "Many of us who work in analytical fields are not trained in even simple interpretation of natural language," write Julia Silge, Ph.D., and David Robinson, Ph.D., in their newly released book Text Mining with R: A tidy approach. The applications of text mining are numerous and varied, though; sentiment analysis can assess the emotional content of text, frequency measurements can identify a document's most important terms, analysis can explore relationships and connections between words, and topic modeling can classify and cluster similar documents. I recently caught up with Silge and Robinson to discuss how they're using text mining on job postings at Stack Overflow, some of the challenges and best practices they've experienced when mining text, and how their tidytext package for R aims to make text analysis both easy and informative. Text and other unstructured data is increasingly important for data analysts and data scientists in diverse fields from health care to tech to nonprofits. This data can help us make good decisions, but to capitalize on it, we must have the tools and the skills to get from unstructured text to insights.

We use and compare various different methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a csv file of type tweet_id,sentiment,tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in "". Similarly, the test dataset is a csv file of type tweet_id,tweet. Please note that csv headers are not expected and should be removed from the training and test datasets. There are some general library requirements for the project and some which are specific to individual methods.