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In the past years, the Web has become a huge source of opinionative data. Social media, such as Twitter, are regarded as public diaries, where millions of people express their sentiments and opinions in their daily interaction. One of the biggest challenges in the analysis of such data, is the classification of their polarity, that is, whether they carry a positive or negative connotation. For this...
Sentiment analysis is a hard problem, while multilingual sentiment analysis is even harder due to the different expression styles in different languages. Although many methods for multilingual sentiment analysis have been developed in the open literature, most of them suffer from two major problems. The first is their excessive dependence on external tools or resources (e.g., Machine translation systems...
Social media analysis constitutes a scientific field that is rapidly gaining ground due to its numerous research challenges and practical applications, as well as the unprecedented availability of data in real time. Several of these applications have significant social and economical impact, such as journalism, crisis management, advertising, etc. However, two issues regarding these applications have...
Nowadays the World Wide Web has evolved into a leading communication channel and information exchange medium. Especially after the introduction of the so-called web 2.0 and the explosion that followed regarding user generated content, the amount of data available over the internet has attracted the interest of both the scientific and business community. Their efforts focus on identifying the inner...
Emotional Polarity Classification is an important task in Sentiment Analysis area. It is applied in many real problems such as reviews of consumer products and services, financial markets, and forensic analysis. The scientists from the areas of text mining and nature language processing have studied how to solve emotional polarity classification problem. They used a variety of methods, from simple...
Discourse markers not only express some sorts of relations between two arguments, but also entail sentiment information. In this paper, we investigate the associations between the relation type and the sentiment polarity of Chinese discourse markers based on a web scale corpus. We present an approach to mining information from a large scale corpus, show the polarity distributions of sentences under...
Work on sentiment analysis has thus far been limited in the news article domain. This has mainly been caused by 1) news articles lacking a clearly defined target, 2) the difficulty in separating good and bad news from positive and negative sentiment, and 3) the seeming necessity of, and complexity in, relying on domain-specific interpretations and background knowledge. In this paper we propose, define,...
In a polarized society, rhetorical arguments are usually expressed by strong, extreme terms which by themselves carry a positive or negative sentiment about one side of the social debate or conflict. By detecting extreme terms in a social-political text such as a blog post, we are able to automatically detect the sentiment of the text about polarizing issues in a divided society. On the other hand,...
Reputation analysis is naturally associated to a sentiment analysis task of the targeted named-entities. This analysis leverages on a sentiment lexicon that includes general sentiment words that characterize the general sentiment towards the targeted named-entity. However, in most cases, target entities are themselves part of the sentiment lexicon, creating a loop from which it is difficult to infer...
Many shopping sites provide functions to submit a user review for a purchased item. Reviews of items including stories such as novels and movies sometimes contain spoilers (undesired and revealing plot descriptions) along with the opinions of the review author. In this paper, we propose a system that helps users see reviews without seeing such plot descriptions. This system classifies each sentence...
In this paper we propose a twitter sentiment analytics that mines for opinion polarity about a given topic. Most of current semantic sentiment analytics depends on polarity lexicons. However, many key tone words are frequently bipolar. In this paper we demonstrate a technique which can accommodate the bipolarity of tone words by context sensitive tone lexicon learning mechanism where the context is...
Opinion leaders play an important role in influencing topics of discussion among a group of persons. Hence, identification of opinion leaders has receive recent attention. Specifically, discovering opinion leaders in a Web-based stock message board might be valuable for many investors. Current methods for finding opinion leaders mainly concentrate on a graph of user connections, and thus leads to...
In this paper, we present a novel self-maintaining, domain-independent, and context-sensitive Sentiment Lexicon (SL) which finds and maps opinion words and phrases to a fuzzy sentiment score ranging from strong negative to strong positive. We show that our automatically built SL has advantage over other already existing lexicons in various aspects, namely, reducing the number of word false-matches...
The internet and the Web 2.0 gave rise to a wide variety of user generated content. This caused a massive growth in the amount and availability of opinionated information. This collection of complex, unstructured information is often referred as Big Data. A common practical application of such Big Data is social media sentiment analysis. The general aim of sentiment analysis is to determine/extract...
Sentiment analysis has now become a popular research problem to tackle in NLP field. However, there are very few researches conducted on sentiment analysis for Chinese. Progress is held back due to lack of large and labelled corpus and powerful models. To remedy this deficiency, we build a Chinese Sentiment Treebank over social data. It concludes 13550 labeled sentences which are from movie reviews...
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