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.
Sentiment analysis research has predominantly been on English texts. Thus there exist many sentiment resources for English, but less so for other languages. Approaches to improve sentiment analysis in a resource-poor focus language include: (a) translate the focus language text into a resource-rich language such as English, and apply a powerful English sentiment analysis system on the text, and (b) translate resources such as sentiment labeled corpora and sentiment lexicons from English into the focus language, and use them as additional resources in the focus-language sentiment analysis system. In this paper we systematically examine both options. We use Arabic social media posts as stand-in for the focus language text. We show that sentiment analysis of English translations of Arabic texts produces competitive results, w.r.t. Arabic sentiment analysis. We show that Arabic sentiment analysis systems benefit from the use of automatically translated English sentiment lexicons. We also conduct manual annotation studies to examine why the sentiment of a translation is different from the sentiment of the source word or text. This is especially relevant for building better automatic translation systems. In the process, we create a state-of-the-art Arabic sentiment analysis system, a new dialectal Arabic sentiment lexicon, and the first Arabic-English parallel corpus that is independently annotated for sentiment by Arabic and English speakers.
This blog was originally featured on blog.aylien.com, a Text Analysis blog with tutorials, Data Visualisations and industry discussions. Our founder, Parsa Ghaffari, gave a talk recently on Natural Language Processing and Sentiment Analysis at the Science Gallery in Dublin. As part of the talk, he put together a nice little example of how you can transform your Google Spreadsheet into a powerful Text Analysis and Data Mining tool. In this case, he took a simple example of analyzing restaurant reviews from a popular review site but the same could be done for hotels, products, service offerings and so on. He wanted to show how easy it can be for data geeks and even the less technical marketers among us, to start analyzing text and gathering business insight from the reams of textual data online today.
Last week I attended IBM (IBM) World of Watson as both a speaker and an attendee, and today as I sit in my neighborhood Starbucks (SBUX) thinking about everything, all I can say is WOW! This was one of the most interesting, inspiring and amazing events I have ever attended. And we are still in the very early stages of Watson, Cognitive and AI. I invite you to follow me as I learn more and write more about the wonderful world of Watson, all the companies that work with it and how it will change our industries, our businesses and our lives. As a wireless analyst and columnist, I come at this world of Watson from the wireless, telecom, internet and television angle.