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part of a trending discussion topic by the presence of a tagged keyword. Relying solely on this keyword, however, may be inadequate for identifying all the discussion associated with a trend. Our research demonstrates that machine learning techniques can be used identify the top trend a tweet belongs to with up to 85
. A previous study showed the possibility of early detection of network problems by monitoring Twitter. However, since Twitter includes many conversation topics, it is difficult to find tweets that relate to network problems. Searching by a particular keyword is insufficient since it produces a lot of false positive
This paper presents strategy to classify tweets sentiment using Naive Bayes techniques based on trainers' perception into three categories; positive, negative or neutral. 50 tweets of ‘Malaysia’ and ‘Maybank’ keywords were selected from Twitter for perception training. In this study, there were 27 trainers
) based method to determine informative tweet and the real-time event detection algorithm to detect the timely occurrence of the given event. In this study, CNN model trained from the tweets related to the earthquake in the past labeled by crowdsourcing plays a role as the classifier to predict an earthquake keyword related
information overload. Analyzing social audience who are interested in a company of social media is very difficult and so many text mining methods e.g. fuzzy keyword match method, Twitter LDA method and Machine learning approaches are used for solving this problem. Using the tweets of the account owner to segment followers and
temporal changes of brand-related keyword networks. Our analysis enables trends in brand awareness to be systematically traced and evaluated. This allows various other analyses, such as advantages and disadvantages of the brand, and a comparison with its competitors.
better service quality. This study aims to measure GO-JEK and Grab customer satisfaction through sentiment analysis of Twitter's data. Both companies use Twitter to reach their customers and promote their service. We collect 126,405 tweets from February to March 2016 containing GO-JEK and Grab keywords. Then, we pre-process
Age predictive analysis is to predict the age of the users who posted the message in any microblog. By using some keywords, we extract the messages as dataset and processed for predicting the age of the user. Here, the design and techniques to foreseen the age of the user by microblog dataset are presented. In recent
enter the keywords that describe the topic of interest and to present the results in several levels of detail. To meet the second requirement, the system uses a Naïve Bayes classifier to identify the sentiments of tweets, as the literature shows that this algorithm combines a good classificatory performance and low
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
with an accuracy of 79.26% is better than SVM with an accuracy of 69.32%. In summary, SSPs are younger, have more statuses, more tweets in succession, and contain keywords that differentiate a spam profile from a non-spam profile.
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
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