8,789 research outputs found
Emotion and polarity prediction from Twitter
Classification of public information from microblogging and social networking services could yield interesting outcomes and insights into the social and public opinions towards different services, products, and events. Microblogging and social networking data are one of the most helpful and proper indicators of public opinion. The aim of this paper is to classify tweets to their classes using cross validation and partitioning the data across cities using supervised machine learning algorithms. Such an approach was used to collect real time Twitter microblogging data tweets towards mentioning iPad and iPhone in different locations in order to analyse and classify data in terms of polarity: positive or negative, and emotion: anger, joy, sadness, disgust, fear, and surprise. We have collected over eighty thousand tweets that have been pre-processed to generate document level ground-truth and labelled according to Emotion and Polarity. We also compared some approaches in order to measures the performance of K-NN, Nave Bayes, and SVM classifiers. We found that the K-NN, Nave Bayes, SVM, and ZeroR have a reasonable accuracy rates, however, the K-NN has outperformed the Nave Bayes, SVM, and ZeroR based on the achieved accuracy rates and trained model time. The K-NN has achieved the highest accuracy rates 96.58% and 99.94% for the iPad and iPhone emotion data sets using cross validation technique respectively. Regarding partitioning the data per city, the K-NN has achieved the highest accuracy rates 98.8% and 99.95% for the iPad and iPhone emotion data sets respectively. Regarding the polarity data sets using both cross validation and partitioning data per city, the K-NN achieved 100% for the all polarity datasets
Identifying Purpose Behind Electoral Tweets
Tweets pertaining to a single event, such as a national election, can number
in the hundreds of millions. Automatically analyzing them is beneficial in many
downstream natural language applications such as question answering and
summarization. In this paper, we propose a new task: identifying the purpose
behind electoral tweets--why do people post election-oriented tweets? We show
that identifying purpose is correlated with the related phenomenon of sentiment
and emotion detection, but yet significantly different. Detecting purpose has a
number of applications including detecting the mood of the electorate,
estimating the popularity of policies, identifying key issues of contention,
and predicting the course of events. We create a large dataset of electoral
tweets and annotate a few thousand tweets for purpose. We develop a system that
automatically classifies electoral tweets as per their purpose, obtaining an
accuracy of 43.56% on an 11-class task and an accuracy of 73.91% on a 3-class
task (both accuracies well above the most-frequent-class baseline). Finally, we
show that resources developed for emotion detection are also helpful for
detecting purpose
- …