The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
enhances the machine learning based Stanford CoreNLP Part-of-Speech (POS) tagger with the Twitter model to extract essential keywords from a tweet. The system was enhanced using two rule-based parsers and a corpus. The research was conducted using tweets of customer service requests sent to a telecommunication company. A
this problem by automatically dividing the social network of a Twitter user into personal cliques, and annotating each clique with keywords to identify the common ground of a clique. Our proposed clique annotation method extracts keywords from the tweet history of the clique members and individually weights the extracted
Twitter, as a social media is a very popular way of expressing opinions and interacting with other people in the online world. When taken in aggregation tweets can provide a reflection of public sentiment towards events. In this paper, we provide a positive or negative sentiment on Twitter posts using a well-known machine learning method for text categorization. In addition, we use manually labeled...
useful features extracted from each Twitter's message. The output is its degree of relevance for each message to Sandy. A number of fuzzy rules are designed and different defuzzification methods are combined in order to obtain desired classification results. We compare the proposed method with the well-known keyword search
target of interest specified by the user from Twitter regardless of their popularity. Assuming that the related observations are likely to contain words people often associate with each other, the associative relations among words are learned from the past messages. When a user gives a keyword representing his/her current
implement the proposed method in three ways that consist of keyword matching designed by hand, machine learning and hybrid of them. Besides, we evaluate classification performance using typical five kinds of event categories. As a result, we confirmed the method of the hybrid has highest average F-score 0.674 in the methods.
present a more informative result compared to conventional search engine. To valid our method, we developed the TCOND system (Twitter Conversation Detector) which offers an alternative, results to keyword search on twitter and Google. We have evaluated our method on collected social network corpus related to specific
keywords from messages posted on social media which will be helpful in the identification of various communities, category of user and hidden pattern present in the social media. In this paper, we applied Probalistic approach to recognize the new keywords and assign the group accordingly. State-of-the-art studies performed
There is no previous research that compares the results of k-means, CLOPE clustering and Latent Dirichlet Allocation (LDA) topic modeling algorithms for detecting trending topics on tweets. Since not all tweets contain hashtags, we considered three training data feature sets: hashtags, keywords and keywords + hashtags
Twitter has become key for bringing awareness about real-world events, but the identification of event related posts goes beyond filtering keywords. Semantic enrichment using knowledge sources such as the Linked Open Data (LOD) cloud, has been proposed to deal with the poor textual contents of tweets for event
Currently microblog search engines have the function to find related users according to input topic keywords. Traditional approaches rank users by their authentication information or their self descriptions (introductions or labels).However, many users may not publish the posts closely related to their certification
interest. The framework utilizes a novel query formulation method in combination with relevance prediction. The query formulation method relies on the construction of a graph of keywords for generating refined queries about the event/news story of interest based on the results of a firststep high precision query. Relevance
filtered. In this paper, we propose a unified spam detection approach, which utilizes external knowledge sources to expand keywords features and applies an ensemble under-sampling based strategy to handle the class-imbalance problem. The experimental results show the effectiveness and robustness of our approach in Weibo data.
present the experiment design to capture and extract the viewing patterns in Twitter using the eye-tracking technology. We show a set of experiment results based on the analysis of eye gazing data, in order to demonstrate how the subjects look for specified keywords in the Twitter timeline, which can further contribute to
feeling or emotions. To deal with the author's feelings, we suggest enhancing a text tweet with an appropriate image, along with/without text. To generate an image from the text, we first analyze the text tweet. The morpheme analyzer detects the key words and then the thumbnail images related to those keywords are retrieved
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.