On this week's If Then, Slate's April Glaser and Will Oremus discuss the outrage at the largest TV-station owner in the country--Sinclair Broadcasting--after the media conglomerate forced its local-news anchors to read a script that echoes Trumpian talking points. They also unpack Trump's beef about Jeff Bezos owning what he calls the #AmazonWashingtonPost. Meanwhile, music streaming site Spotify went public this week in a totally new kind of way. The hosts take a look at its unorthodox move and what it means for the company's future.
I have been trying to use ML for sentiment analysis of sentences, I have been successful with Naive Bayes and SVM but I would like to implement Neural Networks for Sentiment Analysis but couldn't find a way to convert words as input for neural networks. I know that representing word as a numerical is not efficient. How is nlpnet implemented, I tried to understand that but that flew over my head.
Rieis, Julio Cesar Soares dos (Federal University of Minas Gerais) | Souza, Fabrício Benevenuto de (Federal University of Minas Gerais) | Melo, Pedro Olmo S. Vaz de (Federal University of Minas Gerais) | Prates, Raquel Oliveira (Federal University of Minas Gerais) | Kwak, Haewoon (Qatar Computing Research Institute) | An, Jisun (Qatar Computing Research Institute)
A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs. The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations. News websites have to create effective strategies to catch people’s attention and attract their clicks. In this paper we investigate possible strategies used by online news corporations in the design of their news headlines. We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight consecutive months in 2014. In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline. We discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.
Today, users are reading the news through social platforms. These platforms are built to facilitate crowd engagement, but not necessarily disseminate useful news to inform the masses. Hence, the news that is highly engaged with may not be the news that best informs. While predicting news popularity has been well studied, it has not been studied in the context of crowd manipulations. In this paper, we provide some preliminary results to a longer term project on crowd and platform manipulations of news and news popularity. In particular, we choose to study known features for predicting news popularity and how those features may change on reddit.com, asocial platform used commonly for news aggregation. Along with this, we explore ways in which users can alter the perception of news through changing the title of an article. We find that news on Reddit is predictable using previously studied sentiment and content features and that posts with titles changed by Reddit users tend to be more popular than posts with the original article title.
Park, Jaram (Korea Advanced Institute of Science and Technology) | Cha, Meeyoung (Korea Advanced Institute of Science and Technology) | Kim, Hoh (Korea Advanced Institute of Science and Technology) | Jeong, Jaeseung (Korea Advanced Institute of Science and Technology)
Social media has become prominently popular. Tens of millions of users login to social media sites like Twitter to disseminate breaking news and share their opinions and thoughts. For businesses, social media is potentially useful for monitoring the public perception and the social reputation of companies and products. Despite great potential, how bad news about a company influences the public sentiments in social media has not been studied in depth. The aim of this study is to assess people’s sentiments in Twitter upon the spread of two types of information: corporate bad news and a CEO’s apology. We attempted to understand how sentiments on corporate bad news propagate in Twitter and whether any social network feature facilitates its spread. We investigated the Domino’s Pizza crisis in 2009, where bad news spread rapidly through social media followed by an official apology from the company. Our work shows that bad news spreads faster than other types of information, such as an apology, and sparks a great degree of negative sentiments in the network. However, when users converse about bad news repeatedly, their negative sentiments are softened. We discuss various reactions of users towards the bad news in social media such as negative purchase intent.