Although many sentiment lexicons in different languages exist, most are not comprehensive. In a recent sentiment analysis application, we used a large Chinese sentiment lexicon and found that it missed a large number of sentiment words used in social media. This prompted us to make a new attempt to study sentiment lexicon expansion. This paper first formulates the problem as a PU learning probl...

In this paper we present SABRINA (Sentiment Analysis: a Broad Resource for Italian Natural language Applications) a manually annotated prior polarity lexical resource for Italian natural language applications in the field of opinion mining and sentiment induction. The resource consists in two different sets, an Italian dictionary of more than 277.000 words tagged with their prior polarity value...

The web and social media have been growing exponentially in recent years. We now have access to documents bearing opinions expressed on a broad range of topics. This constitutes a rich resource for natural language processing tasks, particularly for sentiment analysis. Nevertheless, sentiment analysis is usually difficult because expressed sentiments are usually topic-oriented. In this paper, w...

Automatic opinion lexicon extraction has attracted lots of attention and many methods have thus been proposed. However, most existing methods depend on dictionaries (e.g., WordNet), which confines their applicability. For instance, the dictionary based methods are unable to find domain dependent opinion words, because the entries in a dictionary are usually domain-independent. There also exist ...

Sentiment classification is a fundamental task in opinion mining. However, most existing systems require a sentiment lexicon to guide sentiment classification, which inevitably suffer from the problem of unknown words. In this paper, we present a morpheme-based fine-to-coarse strategy for Chinese sentence-level sentiment classification. To approach this, we first employ morphological productivi...

This paper describes our approach to the SemEval-2013 task on “Sentiment Analysis in Twitter”. We use simple bag-of-words models, a freely available sentiment dictionary automatically extended with distributionally similar terms, as well as lists of emoticons and internet slang abbreviations in conjunction with fast and robust machine learning algorithms. The resulting system is resource-lean, ...

Traditional sentiment analysis often uses sentiment dictionary to extract sentiment information in text and classify documents. However, emerging informal words and phrases in user generated content call for analysis aware to the context. Usually, they have special meanings in a particular context. Because of its great performance in representing inter-word relation, we use sentiment word vecto...

Traditional sentiment analysis has been focusing on inference of the sentiment polarity using sentiment-bearing words. In this paper, we propose a new way of studying sentiment and capturing ontological changes in a domain specific context in the perspective of computational linguistics using affect proxies. We used Nexis service to create a domain specific corpus focusing on banking sectors. W...

Decision-making in crisis management can benefit from routine monitoring of the (social) media to discover the mass opinion on highly sensitive crisis events. We present an experiment that analyzes Chinese microblog data (extracted from Weibo.cn) to measure sentiment strength and its change in relation to the recent PM 2.5 air pollution events. The data were analyzed using SentiStrength algorit...