| dc.description.abstract | Web-Based Social Media (WBSM) have been on the rise for the recent several years, and have subsequently garnered interest from several groups out to proficiently utilize the vast amounts of data found on these sites. Micro-blogging site Twitter is one of the media sites that have embraced developers and interest groups, Twitter has developed accessible frameworks and allows the use of these frameworks enabling developers access to large amounts of information.
In this master thesis, a system for performing Sentiment Analysis (SA) on Norwegian Tweets is described. The system described uses a two-step binary classification process for subjectivity and polarity classification, utilizing different parameters and three different classifiers - Naive Bayes (NB), Support Vector Machines (SVM), and Maximum Entropy (MaxEnt) - for the two different classification tasks. The solution also makes an attempt at exploiting grammatical metadata given by the NTNU SmartTagger as well as cross-lingual sentiment lookup in the SentiWordNet sentiment lexicon in order to achieve improved results from the classifiers.
A system for extracting sentiment targets after classification is also described. This method is a way of utilizing the classifier in order to identify critical sentiment words in order to augment the detection of the target of a given sentiment in a tweet. | |
