Classifying Short Text in Social Media: Twitter as Case Study
نویسندگان
چکیده
With the huge growth of social media, especially with 500 million Twitter messages being posted per day, analyzing these messages has caught intense interest of researchers. Topics of interest include micro-blog summarization, breaking news detection, opinion mining and discovering trending topics. In information extraction, researchers face challenges in applying data mining techniques due to the short length of tweets as opposed to normal text with longer length documents. Short messages lead to less accurate results. This has motivated investigation of efficient algorithms to overcome problems that arise due to the short and often informal text of tweets. Another challenge that researchers face is stream data, which refers to the huge and dynamic flow of text generated continuously from social media. In this paper, we discuss the possibility of implementing successful solutions that can be used to overcome the inconclusiveness of short texts. In addition, we discuss methods that overcome stream data problems.
منابع مشابه
Team UKNLP: Detecting ADRs, Classifying Medication Intake Messages, and Normalizing ADR Mentions on Twitter
This paper describes the systems we developed for all three tasks of the 2nd Social Media Mining for Health Applications Shared Task at AMIA 2017. The first task focuses on identifying the Twitter posts containing mentions of adverse drug reactions (ADR). The second task focuses on automatic classification of medication intake messages (among those containing drug names) on Twitter. The last ta...
متن کاملText Analytics of Customers on Twitter: Brand Sentiments in Customer Support
Brand community interactions and online customer support have become major platforms of brand sentiment strengthening and loyalty creation. Rapid brand responses to each customer request though inbound tweets in twitter and taking proper actions to cover the needs of customers are the key elements of positive brand sentiment creation and product or service initiative management in the realm of ...
متن کاملEMOTEX: Detecting Emotions in Twitter Messages
Social media and microblog tools are increasingly used by individuals to express their feelings and opinions in the form of short text messages. Detecting emotions in text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or public mood of a community. In this paper, we propose a new approach for automatically classifying text m...
متن کاملA Study on the Use of Social Media to Understand Consumer Preference: The Case of Starbucks
The paper seeks to identify Starbuck's experience in using social media, understand how social media is linked to customer knowledge management, and assess how social media services could have contributed to Starbucks success. Starbucks demonstrates versatility to engage customers and support different part of customer knowledge management strategy through various social media platforms, such a...
متن کاملCrossing Media Streams with Sentiment: Domain Adaptation in Blogs, Reviews and Twitter
Most sentiment analysis studies address classification of a single source of data such as reviews or blog posts. However, the multitude of social media sources available for text analysis lends itself naturally to domain adaptation. In this study, we create a dataset spanning three social media sources – blogs, reviews, and Twitter – and a set of 37 common topics. We first examine sentiments ex...
متن کامل