Sentiment classification involves quantifying the affective reaction of a human to a document, media item or an event. Although researchers have investigated several methods to reliably infer sentiment from lexical, speech and body language cues, training a model with a small set of labeled datasets is still a challenge. For instance, in expanding sentiment analysis to new languages and cultures, it may not always be possible to obtain comprehensive labeled datasets. In this paper, we investigate the application of semi-supervised and transfer learning methods to improve performances on low resource sentiment classification tasks. We experiment with extracting dense feature representations, pre-training and manifold regularization in enhancing the performance of sentiment classification systems. Our goal is a coherent implementation of these methods and we evaluate the gains achieved by these methods in matched setting involving training and testing on a single corpus setting as well as two cross corpora settings. In both the cases, our experiments demonstrate that the proposed methods can significantly enhance the model performance against a purely supervised approach, particularly in cases involving a handful of training data.

Facebook's data-sharing deals with device-makers included China's Huawei -- a company viewed with suspicion by U.S. intelligence agencies. Here, an ad for the Huawei P20 smartphone is seen in China last month. Facebook's data-sharing deals with device-makers included China's Huawei -- a company viewed with suspicion by U.S. intelligence agencies. Here, an ad for the Huawei P20 smartphone is seen in China last month. Facebook's longstanding agreements that led it to share users' data with device-makers included Chinese phone-maker Huawei – a company of which the U.S. government has long been suspicious, and which intelligence officials view as a security threat.

If you haven't done this already, do it now. In Settings, hit the Privacy tab. From here, you can control who gets to see your future posts and friends list. Choose from Public, Friends, Only Me and Custom in the dropdown menu. Annoyingly, changing this has no effect on who's able to see your past Facebook posts.

NPR's Mary Louise Kelly speaks with New York Times reporter Michael LaForgia about the his story about Facebook sharing user data with several hundred companies.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Facebook gave at least 60 device manufacturers, including Apple, Blackberry, Samsung, Amazon, and Microsoft, access to huge amounts of data about users and their friends, the New York Times reported on Sunday. These companies in some cases received access to information about a user's religion, political views, relationship statuses, and other personal details. The manufacturers also reportedly got access to information on users' friends, even if they tried to prohibit their data from being shared with third parties. Facebook recently landed in hot water when revelations surfaced in March that a third-party quiz app was able to collect information from users and their friends.

Starting with the idea that sentiment analysis models should be able to predict not only positive or negative but also other psychological states of a person, we implement a sentiment analysis model to investigate the relationship between the model and emotional state. We first examine psychological measurements of 64 participants and ask them to write a book report about a story. After that, we train our sentiment analysis model using crawled movie review data. We finally evaluate participants' writings, using the pretrained model as a concept of transfer learning. The result shows that sentiment analysis model performs good at predicting a score, but the score does not have any correlation with human's self-checked sentiment.

Over years, a crucial part of data-gathering behavior has revolved around what other people think. With the constantly growing popularity and availability of opinion-driven resources such as personal blogs and online review sites, new challenges and opportunities are emerging as people have started using advanced technologies to make decisions now. Sentiment analysis or opinion mining, refers to the use of computational linguistics, text analytics and natural language processing to identify and extract information from source materials. Sentiment analysis is considered one of the most popular applications of text analytics. The primary aspect of sentiment analysis includes data analysis on the body of the text for understanding the opinion expressed by it and other key factors comprising modality and mood.

Cambridge Analytica was caught tampering with elections by exploiting Facebook, but chances are that this is the tip of the iceberg, and that many others, including scammers and ID thieves, are also exploiting Facebook and other social networks. One way that they do this is as follows. Also, scammers use dozens if not hundreds of IP addresses to create these numerous fake accounts. They do it by recruiting an army of drone workers paid peanuts, or via a Botnet, or recycled or non-static IP addresses, or proxy servers. The smartest ones might even use computer viruses to create Facebook accounts in the background on your hijacked computer (thus via your IP address), without you being aware of it.

More often than not, the best initial use case for AI won't be the company's biggest problem. Making AI real means going beyond the hype, focusing on what is doable in a defined timeframe, with the budget, resources and data that are available. By doing this, firms often discover a more specific use case than they initially considered. For example, instead of trying to improve prediction of customer demand overall, they start with sentiment analysis on social media to establish a better customer dialogue process. It doesn't matter how big the initial use case is.

We recently announced a new version of Excel Add-in which lets you perform state-of-the-art text analysis capabilities from the comforts of your spreadsheets without writing a single line of code. The add-in has been received very well by users working across different industry verticals like Market Research, Software, Consumer Goods, Education, etc. solving a variety of use-cases. Sentiment analysis has been the most used function of our Excel add-in closely followed by Emotion detection. Many of our users use sentiment analysis in Excel to quickly and accurately analyze the responses of their open-ended surveys, online chatter around their product/service or to analyze product reviews from e-commerce sites. In this blog post, we will discuss how to use the function Sentiment Analysis in Excel Add-in to do text analytics for any type of content.