Study On Public Sentiment Analysis Of Events In Microblogs

Microblogging service is a new social medium on the Internet. Microblog usersshare their personal experiences and opinions in short texts, and express their attitudestowards social events. Therefore, it is important to discover popular and breaking eventsand analyze the public sentiments of them in microblogs. However, microblog analysis isdifficult: The texts of microblogs are short with diverse topics, and are expressed in freeforms which are usually informal. Thus, it is valuable to conduct academic research onpublic sentiment analysis of events in microblogs. The main contributions of this thesisinclude:Using hashtags as clues to discover breaking events in Microblogs. This methodintroduces three measurements, including Instability, Twitter Meme Possibility and Au-thorship Entropy on hashtags, which are closely relevant to the topic of the texts but notdependent on the words. By classifying the hashtags, the method recognizes breakingpopular events relevant to some real social events, removes the noises brought by onlinetopics and advertisements in multilingual microblog messages. It overcomes traditionalburst detection methods which ignore the text contents, as well as topic detection meth-ods which rely on the semantic information. Experimental results show that it achieves ahigher classification performance than other hashtag classification methods.Using emotion tokens as sentiment units to construct a sentiment lexicon forsentiment analysis. The emotion tokens include emotion symbols, repeating lettersand repeating punctuations, which frequently occur in informal Internet texts. Their co-occurrences can be utilized to automatically construct sentiment lexicons by label propa-gation algorithms. Comparing with traditional methods, the proposed method makes useof the emotion tokens typically in microblogs. It is not restricted to any single language ordomain, thus behaves better in multilingual microblog sentiment analysis as experimentalresults have indicated.Facing the characteristics of event-related Chinese microblogs, constructingChinese microblog sentiment lexicons with out-of-vocabulary (OOV) words discov-ery methods, which are used for sentiment analysis of events. The proposed methodreduces the errors brought by traditional word segmentation tools and semantic depen-dencies. It discovers the sentiment polarities of OOV words, animated emotional icons, misspelled words and named entities, which are formed by the public opinions from themicroblog users but are typically excluded in traditional sentiment lexicons. Experimen-tal results show that the performances are higher when considering the entries from theconstructed sentiment lexicon. Besides, this method can be applied to construct lexiconswith more dimensions (such as happiness, anger, sadness, fear and surprise) other thanthe only positive and negative sentiments.