Abstract. Determining polarity of words is an important task in sentiment anal-ysis with applications in several areas such as text categorization and review analysis. In this paper, we propose a multilingual approach for word polarity detection. We construct a word relatedness graph by using the relations in WordNet of a given language. We extend the graph by connecting the Word-Nets of different languages with the help of the Inter-Lingual-Index based on English WordNet. We develop a semi-automated procedure to produce a set of positive and negative seed words for foreign languages by using a set of English seed words. To identify the polarity of unlabeled words, we propose a method based on random walk model with commute time metric as p...
For fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compar...
16th Workshop, CLSW 2015, Beijing, China, May 9-11, 2015In sentiment analysis, polarity shifting mea...
Abstract. Along with the proliferation of new media, the user generated content becomes irreplaceabl...
Automatically identifying the sentiment polarity of words is a very important task that has been use...
Due to the huge volume and linguistic variation of data shared online, accurate detection of the sen...
In this paper we focus on the creation of general-purpose (as opposed to domain-specific) polarity l...
In this paper we focus on the creation of general-purpose (as opposed to domain-specific) polarity l...
Recent advancements in machine translation foster an interest of its use in sentiment analysis. In t...
Sentiment analysis aims to extract the sentiment polarity of given segment of text. Polarity resourc...
The increasing amount of sentiments disseminated by traditional and social media and their impact on...
In this paper we explore two approaches for the automatic annotation of polarity (positive, negative...
Recent interests in Sentiment Analysis brought the attention on effective methods to detect opinions...
Abstract. In recent years a variety of approaches in classifying the sen-timent polarity of texts ha...
Social media contains a lot of emotional information. How to accurately and efficiently recognise th...
Many automatic opinion mining methods make use of a lexicon in which each word is associated with a ...
For fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compar...
16th Workshop, CLSW 2015, Beijing, China, May 9-11, 2015In sentiment analysis, polarity shifting mea...
Abstract. Along with the proliferation of new media, the user generated content becomes irreplaceabl...
Automatically identifying the sentiment polarity of words is a very important task that has been use...
Due to the huge volume and linguistic variation of data shared online, accurate detection of the sen...
In this paper we focus on the creation of general-purpose (as opposed to domain-specific) polarity l...
In this paper we focus on the creation of general-purpose (as opposed to domain-specific) polarity l...
Recent advancements in machine translation foster an interest of its use in sentiment analysis. In t...
Sentiment analysis aims to extract the sentiment polarity of given segment of text. Polarity resourc...
The increasing amount of sentiments disseminated by traditional and social media and their impact on...
In this paper we explore two approaches for the automatic annotation of polarity (positive, negative...
Recent interests in Sentiment Analysis brought the attention on effective methods to detect opinions...
Abstract. In recent years a variety of approaches in classifying the sen-timent polarity of texts ha...
Social media contains a lot of emotional information. How to accurately and efficiently recognise th...
Many automatic opinion mining methods make use of a lexicon in which each word is associated with a ...
For fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compar...
16th Workshop, CLSW 2015, Beijing, China, May 9-11, 2015In sentiment analysis, polarity shifting mea...
Abstract. Along with the proliferation of new media, the user generated content becomes irreplaceabl...