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Recently, with the increasing of users and activities in social network service, an image sentiment analysis has been an important keyword for psychological study and commercial marketing. To recognize accurately user's sentiments of the image, it is essential to identify discriminative visual features and then
Social media is now playing a vital role in influencing people's sentiment in favor or against a government or an organization. Therefore, to understand the sentiment of any posting in social media, an efficient method is an ultimate necessity. We have analyzed some facebook postings to understand political sentiments
In this paper, we propose a new method of classifying tendencies and opinions in texts of multiple sentence length extracted from social media and covering both formal and informal vocabularies. To extract contextual information from the texts, we carry out computations based on keywords, the position of the sentence
Question Understanding of Chinese Question-Answering System generally includes steps such as: word segmentation, POS Tagging, keywords expansion, information retrieval etc. The extended keyword set usually has redundant messages and part of the words and phrases may be not relevant to the question. Consequently
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
analysis platform to detect the global topics. Our framework targets various countries' social media and extracts keywords from messages written by different languages. Then the framework translates keywords from a local language to English, so that we can understand meanings of keywords. Since our framework is based on
Adverse drug reactions (ADRs) detection is critical to avoid malpractices yet challenging due to its uncertainty in pre-marketing review and the underreporting in post-marketing surveillance. To conquer this predicament, social media based ADRs detection methods have been proposed recently. However, existing
Twitter and social media as a whole has great potential as a source of disease surveillance data however the general messiness of tweets presents several challenges for standard information extraction methods. Current methods for disease surveillance on twitter rely on inflexible keyword based approaches that require
The growing popularity of social network services has led to many studies of various phenomena in this area. However, most of this research has been conducted using English language data, and relatively little has considered Korean. In this paper, we demonstrate a systematic analysis framework using Korean Twitter
favorite restaurant. The sentiment analysis for restaurant rating system rates the restaurant depending upon the reviews given by the users. The system breaks user comments to check for sentiment keywords. Once the keywords are found, it associates the comment with a sentiment rank. Sentiment analysis can also be extended
simple combinations of keywords, e.g., disjunction of keywords. The recent breakthrough in fully homomorphic encryption has allowed us to construct arbitrary searching criteria theoretically. In this paper, we consider a (t, n) threshold query, which searches for documents containing more than t out of n keywords. This form
news sites discussing extremism and finally sites with no discussion of extremism. Then parts of speech tagging was used to find the most frequent keywords in these pages. Utilizing sentiment software in conjunction with classification software a decision tree that could effectively discern which class a particular page
overall life time. The "bag-of-words" model is used to represent the content of an article as a vector of features, which are uni-, bi-, or tri-gram keywords. The thesaurus approach is applied to group words with similar meanings to a set of root words to reduce the size of the feature space. Normalized TF-IDF
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