Twitter Sentiment Analysis: Application for Classifying Tweets with Video Games as Keywords

The growth of microblogging services has expanded exponentially in recent years for mining user opinions. Sentiment analysis was applied to classify Twitter posts with video game titles as keywords. An analysis of the blog history, words and sentiments associated with the blog can help reveal whether the particular game is ‘violent’ and stress inducing or ‘non-violent’ and benign. An application was developed to collect and clean data. Naïve Bayes algorithm was applied to the cleaned data to determine the polarity of the words on the data to come to a conclusion whether, based on the words of the tweet, the particular game could be classified as ‘violent’ or ‘non-violent’. The results of the algorithm are analysed for accuracy, precision and recall. Deep learning models are discussed for use in future to improve accuracy

Predicting civil unrest by categorizing Dutch Twitter Events

We propose a system that assigns topical labels to automatically detected events in the Twitter stream. The automatic detection and labeling of events in social media streams is challenging due to the large number and variety of messages that are posted. The early detection of future social events, specifically those associated with civil unrest, has a wide applicability in areas such as security, e-governance, and journalism. We used machine learning algorithms and encoded the social media data using a wide range of features. Experiments show a high-precision (but low-recall) performance in the first step. We designed a second step that exploits classification probabilities, boosting the recall of our category of interest, social action events.</p

Open-domain extraction of future events from Twitter

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Dataset: tweets and events linked to the paper 'Open-domain extraction of future events from Twitter'

Input data and output of research conducted in the study described in the paper: F. Kunneman and A. Van den Bosch (2016), Open-domain extraction of future events from Twitter, Natural Language Engineering, doi: 10.1017/S1351324916000036 The paper describes a system that extracts future referring time expressions and entities from Twitter messages, and subsequently detects events as a pair of a date and entity the are often mentioned in the same tweet. This dataset features the ids of a large set of Dutch tweets posted in August 2014, which was used as input to the system, as well as the time expression and / or entity that was extracted from each tweet, if any. Furthermore, the detected events are included, represented as a date, one or more describing terms, the tweetids that refer to it and the assessment of the event by human annotators

Dataset: tweets and events linked to the paper 'Open-domain extraction of future events from Twitter'

Item does not contain fulltextInput data and output of research conducted in the study described in the paper: F. Kunneman and A. Van den Bosch (2016), Open-domain extraction of future events from Twitter, Natural Language Engineering, doi: 10.1017/S1351324916000036 The paper describes a system that extracts future referring time expressions and entities from Twitter messages, and subsequently detects events as a pair of a date and entity the are often mentioned in the same tweet. This dataset features the ids of a large set of Dutch tweets posted in August 2014, which was used as input to the system, as well as the time expression and / or entity that was extracted from each tweet, if any. Furthermore, the detected events are included, represented as a date, one or more describing terms, the tweetids that refer to it and the assessment of the event by human annotators