Guha, Satarupa (International Institute of Information Technology, Hyderabad) | Chakraborty, Tanmoy (University of Maryland, College Park) | Datta, Samik (Flipkart Internet Pvt. Ltd.) | Kumar, Mohit (Flipkart Internet Pvt. Ltd.) | Varma, Vasudeva (International Institute of Information Technology, Hyderabad)
An overwhelming amount of data is generated everyday onsocial media, encompassing a wide spectrum of topics. With almost every business decision depending on customer opinion, mining of social media data needs to be quick and easy.For a data analyst to keep up with the agility and the scale of the data, it is impossible to bank on fully supervised techniques to mine topics and their associated sentiments from social media. Motivated by this, we propose a weakly supervised approach (named, TweetGrep) that lets the data analyst easily define a topic by few keywords and adapt a generic sentiment classifier to the topic – by jointly modeling topics and sentiments using label regularization. Experiments with diverse datasets show that TweetGrep beats the state-of-the-art models for both the tasks of retrieving topical tweet sand analyzing the sentiment of the tweets (average improvement of 4.97% and 6.91% respectively in terms of area under the curve). Further, we show that TweetGrep can also be adopted in a novel task of hashtag disambiguation, which significantly outperforms the baseline methods.
Tanev, Hristo (Joint Research Centre, European Commission) | Ehrmann, Maud (Joint Research Centre, European Commission) | Piskorski, Jakub (Frontex) | Zavarella, Vanni (Joint Research Centre, European Commission)
We describe a simple IR approach for linking news about events, detected by an event extraction system, to messages from Twitter (tweets). In particular, we explore several methods for creating event-specific queries for Twitter and provide a quantitative and qualitative evaluation of the relevance and usefulness of the information obtained from the tweets. We showed that methods based on utilization of word co-occurrence clustering, domain-specific keywords and named entity recognition improve the performance with respect to a basic approach.
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.
Apple is set to hold its biggest software event of the year, WWDC, in the middle of June. It'll use the San Francisco event to show off all of the software that's on its way to your Watch, phone and other computers – as well as potentially new Apple devices. The event comes at a big time for Apple. The company is fresh off the back of its first quarter of decline since the iPhone came out, and is feeling the heat from other companies like Google. It will intend to use WWDC as a way of showcasing the software and potentially other products that it hopes will prove its doubters wrong and get the company to grow again.
Its venerable phone line wasn't the only newly minted product Apple showed off at the iPhone 8 event on Tuesday. Eddie Cue announced onstage that the company will expand availability of its TV app to seven new countries by the end of the year and will be adding local news and sports programming as well. The TV app will be available in Australia and Canada next month, the spread to Germany, France, Sweden, Norway and the UK by the end of the year. US sports fans (that is, those that live in the country), will be able to track their favorite teams and have Apple TV push an on-screen notification whenever a game starts. By the end of the year, Apple also announced that users will be able to ask Siri directly to switch to a game.